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Operti, Felipe Gioachino. "Interpolation strategy based on Dynamic Time Warping." reponame:Repositório Institucional da UFC, 2015. http://www.repositorio.ufc.br/handle/riufc/11446.
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OPERTI, Felipe Gioachino. Interpolation strategy based on Dynamic Time Warping. 2015. 53 f. Dissertação (Mestrado em Física) - Programa de Pós-Graduação em Física, Departamento de Física, Centro de Ciências, Universidade Federal do Ceará, Fortaleza, 2015.Submitted by Edvander Pires (edvanderpires@gmail.com) on 2015-04-14T22:11:14Z No. of bitstreams: 1 2015_dis_fgoperti.pdf: 5361657 bytes, checksum: b47dae9c4d72accf5fe2c50b89abaae4 (MD5)
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In oil industry, it is essential to have the knowledge of the stratified rocks’ lithology and, as consequence, where are placed the oil and the natural gases reserves, in order to efficiently drill the soil, without a major expense. In this context, the analysis of seismological data is highly relevant for the extraction of such hydrocarbons, producing predictions of profiles through reflection of mechanical waves in the soil. The image of the seismic mapping produced by wave refraction and reflection into the soil can be analysed to find geological formations of interest. In 1978, H. Sakoe et al. defined a model called Dynamic Time Warping (DTW)[23] for the local detection of similarity between two time series. We apply the Dynamic Time Warping Interpolation (DTWI) strategy to interpolate and simulate a seismic landscape formed by 129 depth-dependent sequences of length 201 using different values of known sequences m, where m = 2, 3, 5, 9, 17, 33, 65. For comparison, we done the same operation of interpolation using a Standard Linear Interpolation (SLI). Results show that the DTWI strategy works better than the SLI when m = 3, 5, 9, 17, or rather when distance between the known series has the same order size of the soil layers.
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Sinkus, Skirmantas. "Kinect įrenginiui skirtų gestų atpažinimo algoritmų tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140806_143213-09689.
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Microsoft Kinect įrenginys išleistas tik 2010 metais. Jis buvo skirtas Microsoft Xbox 360 vaizdo žaidimų konsolei, vėliau 2012 metais buvo pristatytas Kinect ir Windows personaliniams kompiuteriams. Taigi tai palyginus naujas įrenginys ir aktualus šiai dienai.
Daugiausiai yra sukurta kompiuterinių žaidimų, kurie naudoja Microsoft Kinect įrenginį, bet šį įrenginį galima panaudoti daug plačiau ne tik žaidimuose, viena iš sričių tai sportas, konkrečiau treniruotės, kurias būtų galima atlikti namuose.
Šiuo metu pasaulyje yra programinės įrangos, žaidimų, sportavimo programų, kuri leidžia kontroliuoti treniruočių eigą sekdama ar žmogus teisingai atlieka treniruotėms numatytus judesius. Kadangi Lietuvoje panašios programinės įrangos nėra, taigi reikia sukurti įrangą, kuri leistų Lietuvos treneriams kurti treniruotes orientuotas į šio įrenginio panaudojimą.
Šio darbo pagrindinis tikslas yra atlikti Kinect įrenginiui skirtų gestų atpažinimo algoritmų tyrimą, kaip tiksliai jie gali atpažinti gestus ar gestą. Pagrindinis dėmesys skiriamas šiai problemai, taip pat keliami, bet netyrinėjami kriterijai kaip atpažinimo laikas, bei realizacijos sunkumas.
Šiame darbe sukurta programa, judesius bei gestus atpažįsta naudojant Golden Section Search algoritmą. Algoritmas palygina du modelius ar šablonus, ir jei neranda atitikmens, tai pirmasis šablonas šiek tiek pasukamas ir lyginimo procesas paleidžiamas vėl, taipogi tam tikro kintamojo dėka galime keisti algoritmo tikslumą. Taipogi... [toliau žr. visą tekstą]Microsoft Kinect device was released in 2010. It was designed for Microsoft Xbox 360 gaming console, later on in 2012 was presented Kinect device for Windows personal computer. So this device is new and current. Many games has been created for Microsoft Kinect device, but this device could be used not only in games, one of the areas where we can use it its sport, specific training, which can be performed at home. At this moment in world are huge variety of games, software, training programs which allows user to control training course by following a person properly perform training provided movements. Since in Lithuania similar software is not available, so it is necessary to create software that would allow Lithuania coaches create training focused on the use of this device. The main goal of this work is to perform research of the Kinect device gesture recognition algorithms to study exactly how they can recognize gestures or gesture. It will focus on this issue mainly, but does not address the criteria for recognition as the time and difficulty of realization. In this paper, a program that recognizes movements and gestures are using the Golden section search algorithm. Algorhithm compares the two models or templates, and if it can not find a match, this is the first template slightly rotated and comparison process is started again, also a certain variable helping, we can modify the algorithm accuracy. Also for comparison we can use Hidden Markov models algorhithm received... [to full text]
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Кононенко, Олексій Сергійович. "Дослідження системи розпізнавання голосових сигналів в умовах обмеженої обчисленої потужності." Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23167.
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Актуальність теми. Зараз систем розпізнавання мовлення набувають все більшої популярності та зустрічаються все частіше. Успішними прикладами використання технології розпізнавання мови в мобільних додатках є: введення адреси голосом в Яндекс.Навігатор, голосовий пошук Google Now.
Крім мобільних пристроїв, технологія розпізнавання мови знаходить широке поширення в різноманітних сферах людської діяльності:
● Телефонія: автоматизація обробки вхідних і вихідних дзвінків шляхом створення голосових систем самообслуговування зокрема для: отримання довідкової інформації та консультування, замовлення послуг, товарів, зміни параметрів чинних послуг, проведення опитувань, анкетування, збору інформації, інформування та будь-які інші сценарії;
● Рішення "Розумний будинок": голосовий інтерфейс управління системами «Розумний будинок»;
● Побутова техніка і роботи: голосовий інтерфейс електронних роботів; голосове керування побутовою технікою тощо;
● Автомобілі: голосове управління в салоні автомобіля - наприклад, навігаційною системою;
● Соціальні сервіси для людей з обмеженими можливостями;
● Комплексні системи захисту інформації. Голосова аутентифікація.
● Визначення емоційного забарвлення голосу диктора.
Об’єктом дослідження є процес розпізнавання голосових сигналів.
Предметом дослідження є методи та моделі розпізнавання голосових сигналів в умовах обмеженої обчислювальної потужності.
Мета роботи: підвищення ефективності процесу розпізнавання голосових сигналів в умовах обмеженої обчислювальної потужності.
Методи дослідження. В роботі використовуються методи математичного моделювання, методи оптимізації, методи системного аналізу, чисельні методи.Theme urgency. Speech recognition systems are becoming increasingly popular and increasingly common. Successful examples of using speech recognition technology in mobile applications are: entering a voice address in Yandex.Navigator, Google Now voice search. In addition to mobile devices, speech recognition technology is widely used in various areas of human activity: ● Telephony: automates the processing of incoming and outgoing calls by creating voice self-service systems in particular for: receiving background information and advice, ordering services, goods, changing the parameters of current services, conducting surveys, questionnaires, collecting information, informing and any other scenarios; ● “Smart House” solutions: voice interface for intelligent home systems management; ● Household appliances and work: voice interface of electronic robots; voice control of home appliances, etc .; ● Cars: voice control in the car - for example, the navigation system; ● Social services for people with disabilities; ● Comprehensive information security systems. Voice authentication. ● Determination of the emotional color of the speaker's voice. Object of research are systems and algorithms for voice recognition. Subject of research is a usage of dynamic time warping algorithm in speech recognition systems in the conditions of limited computing power Research objective: development and modification of the dynamic time warping algorithm for recognizing a limited vocabulary. Research methods. Methods of mathematical modeling, methods of optimization, methods of system analysis, numerical methods are used in this work.
Актуальность темы. Сейчас системы распознавания речи приобретают все большую популярность и встречаются все чаще. Успешными примерами использования технологии распознавания речи в мобильных приложениях являются: ввод адреса голосом в Яндекс.Навигатор, голосовой поиск Google Now. Кроме мобильных устройств, технология распознавания речи находит широкое распространение в различных сферах человеческой деятельности: ● Телефония: автоматизация обработки входящих и исходящих звонков путем создания голосовых систем самообслуживания в частности для: получения справочной информации и консультирование, заказ услуг, товаров, изменения параметров действующих услуг, проведения опросов, анкетирования, сбора информации, информирование и любые другие сценарии; ● Решение "Умный дом": голосовой интерфейс управления системами «Умный дом»; ● Бытовая техника и работы: голосовой интерфейс электронных роботов голосовое управление бытовой техникой и т.д.; ● Автомобили: голосовое управление в салоне автомобиля - например, навигационной системой; ● Социальные сервисы для людей с ограниченными возможностями; ● Комплексные системы защиты информации. Голосовая аутентификация. ● Определение эмоциональной окраски голоса диктора. Объектом исследования являются системы и алгоритмы распознавания голосовых сигналов. Предметом исследования является алгоритм динамической трансформации временной шкалы в системах распознавания голосовых сигналов в условиях ограниченной вычислительной мощности. Цель работы: разработка и модификация алгоритма динамической трансформации временной шкалы для распознавания ограниченного словаря. Методы исследования. В работе используются методы математического моделирования, методы оптимизации, методы системного анализа, численные методы.
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Fitriani. "Multiscale Dynamic Time and Space Warping." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45279.
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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.Includes bibliographical references (p. 149-151).
Dynamic Time and Space Warping (DTSW) is a technique used in video matching applications to find the optimal alignment between two videos. Because DTSW requires O(N4) time and space complexity, it is only suitable for short and coarse resolution videos. In this thesis, we introduce Multiscale DTSW: a modification of DTSW that has linear time and space complexity (O(N)) with good accuracy. The first step in Multiscale DTSW is to apply the DTSW algorithm to coarse resolution input videos. In the next step, Multiscale DTSW projects the solution from coarse resolution to finer resolution. A solution for finer resolution can be found effectively by refining the projected solution. Multiscale DTSW then repeatedly projects a solution from the current resolution to finer resolution and refines it until the desired resolution is reached. I have explored the linear time and space complexity (O(N)) of Multiscale DTSW both theoretically and empirically. I also have shown that Multiscale DTSW achieves almost the same accuracy as DTSW. Because of its efficiency in computational cost, Multiscale DTSW is suitable for video detection and video classification applications. We have developed a Multiscale-DTSW-based video classification framework that achieves the same accuracy as a DTSW-based video classification framework with greater than 50 percent reduction in the execution time. We have also developed a video detection application that is based on Dynamic Space Warping (DSW) and Multiscale DTSW methods and is able to detect a query video inside a target video in a short time.
by Fitriani.
S.M.
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Hounsinou, Sena Gladys N. "Hardware realization of speech-time warping algorithm /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1650508391&sid=1&Fmt=2&clientId=1509&RQT=309&VName=PQD.
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Júnior, Sylvio Barbon. "Dynamic Time Warping baseado na transformada wavelet." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-15042008-211812/.
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Dynamic Time Warping (DTW) é uma técnica do tipo pattern matching para reconhecimento de padrões de voz, sendo baseada no alinhamento temporal de um sinal com os diversos modelos de referência. Uma desvantagem da DTW é o seu alto custo computacional. Este trabalho apresenta uma versão da DTW que, utilizando a Transformada Wavelet Discreta (DWT), reduz a sua complexidade. O desempenho obtido com a proposta foi muito promissor, ganhando em termos de velocidade de reconhecimento e recursos de memória consumidos, enquanto a precisão da DTW não é afetada. Os testes foram realizados com alguns fonemas extraídos da base de dados TIMIT do Linguistic Data Consortium (LDC)Dynamic TimeWarping (DTW) is a pattern matching technique for speech recognition, that is based on a temporal alignment of the input signal with the template models. One drawback of this technique is its high computational cost. This work presents a modified version of the DTW, based on the DiscreteWavelet Transform (DWT), that reduces the complexity of the original algorithm. The performance obtained with the proposed algorithm is very promising, improving the recognition in terms of time and memory allocation, while the precision is not affected. Tests were performed with speech data collected from TIMIT corpus provided by Linguistic Data Consortium (LDC).
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Coelho, Mariana Sátiro. "Patterns in financial markets: Dynamic time warping." Master's thesis, NSBE - UNL, 2012. http://hdl.handle.net/10362/9539.
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and EconomicsThis work project introduces the performance of the algorithm Dynamic Time Warping amidst trading strategies in the financial markets. The employed procedure allows the comparison between any two sequences of data with different time lengths. Different features for the method were implemented, although those did not improve its promptness or accuracy in the outcomes obtained. Two potential investment strategies are presented within this theme. One yielded satisfactory outcomes whilst the other resulted in inconsistent values. The results point to the possible existence of patterns in the Equity Indexes’ behaviour, as well as their distortion across the time axis.
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Ko, Ming Hsiao. "Using dynamic time warping for multi-sensor fusion." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/384.
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Fusion is a fundamental human process that occurs in some form at all levels of sense organs such as visual and sound information received from eyes and ears respectively, to the highest levels of decision making such as our brain fuses visual and sound information to make decisions. Multi-sensor data fusion is concerned with gaining information from multiple sensors by fusing across raw data, features or decisions. The traditional frameworks for multi-sensor data fusion only concern fusion at specific points in time. However, many real world situations change over time. When the multi-sensor system is used for situation awareness, it is useful not only to know the state or event of the situation at a point in time, but also more importantly, to understand the causalities of those states or events changing over time.Hence, we proposed a multi-agent framework for temporal fusion, which emphasises the time dimension of the fusion process, that is, fusion of the multi-sensor data or events derived over a period of time. The proposed multi-agent framework has three major layers: hardware, agents, and users. There are three different fusion architectures: centralized, hierarchical, and distributed, for organising the group of agents. The temporal fusion process of the proposed framework is elaborated by using the information graph. Finally, the core of the proposed temporal fusion framework – Dynamic Time Warping (DTW) temporal fusion agent is described in detail.Fusing multisensory data over a period of time is a challenging task, since the data to be fused consists of complex sequences that are multi–dimensional, multimodal, interacting, and time–varying in nature. Additionally, performing temporal fusion efficiently in real–time is another challenge due to the large amount of data to be fused. To address these issues, we proposed the DTW temporal fusion agent that includes four major modules: data pre-processing, DTW recogniser, class templates, and decision making. The DTW recogniser is extended in various ways to deal with the variability of multimodal sequences acquired from multiple heterogeneous sensors, the problems of unknown start and end points, multimodal sequences of the same class that hence has different lengths locally and/or globally, and the challenges of online temporal fusion.We evaluate the performance of the proposed DTW temporal fusion agent on two real world datasets: 1) accelerometer data acquired from performing two hand gestures, and 2) a benchmark dataset acquired from carrying a mobile device and performing pre-defined user scenarios. Performance results of the DTW based system are compared with those of a Hidden Markov Model (HMM) based system. The experimental results from both datasets demonstrate that the proposed DTW temporal fusion agent outperforms HMM based systems, and has the capability to perform online temporal fusion efficiently and accurately in real–time.
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Ko, Ming Hsiao. "Using dynamic time warping for multi-sensor fusion." Curtin University of Technology, Department of Computing, 2009. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=129032.
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Fusion is a fundamental human process that occurs in some form at all levels of sense organs such as visual and sound information received from eyes and ears respectively, to the highest levels of decision making such as our brain fuses visual and sound information to make decisions. Multi-sensor data fusion is concerned with gaining information from multiple sensors by fusing across raw data, features or decisions. The traditional frameworks for multi-sensor data fusion only concern fusion at specific points in time. However, many real world situations change over time. When the multi-sensor system is used for situation awareness, it is useful not only to know the state or event of the situation at a point in time, but also more importantly, to understand the causalities of those states or events changing over time.Hence, we proposed a multi-agent framework for temporal fusion, which emphasises the time dimension of the fusion process, that is, fusion of the multi-sensor data or events derived over a period of time. The proposed multi-agent framework has three major layers: hardware, agents, and users. There are three different fusion architectures: centralized, hierarchical, and distributed, for organising the group of agents. The temporal fusion process of the proposed framework is elaborated by using the information graph. Finally, the core of the proposed temporal fusion framework – Dynamic Time Warping (DTW) temporal fusion agent is described in detail.
Fusing multisensory data over a period of time is a challenging task, since the data to be fused consists of complex sequences that are multi–dimensional, multimodal, interacting, and time–varying in nature. Additionally, performing temporal fusion efficiently in real–time is another challenge due to the large amount of data to be fused. To address these issues, we proposed the DTW temporal fusion agent that includes four major modules: data pre-processing, DTW recogniser, class templates, and decision making. The DTW recogniser is extended in various ways to deal with the variability of multimodal sequences acquired from multiple heterogeneous sensors, the problems of unknown start and end points, multimodal sequences of the same class that hence has different lengths locally and/or globally, and the challenges of online temporal fusion.
We evaluate the performance of the proposed DTW temporal fusion agent on two real world datasets: 1) accelerometer data acquired from performing two hand gestures, and 2) a benchmark dataset acquired from carrying a mobile device and performing pre-defined user scenarios. Performance results of the DTW based system are compared with those of a Hidden Markov Model (HMM) based system. The experimental results from both datasets demonstrate that the proposed DTW temporal fusion agent outperforms HMM based systems, and has the capability to perform online temporal fusion efficiently and accurately in real–time.
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Aguiar, Rogerio Oliveira de. "Classificador Automático e Não-Supervisionado de Batimentos Cardíacos Baseado no Algoritmo Dynamic Tiime Warping." Universidade Federal do Espírito Santo, 2008. http://repositorio.ufes.br/handle/10/4066.
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Previous issue date: 2008-07-23O Projeto Telecardio é um projeto de pesquisa em telemonitoramento de pacientes cardíacos e identificação automática de situações de risco. Neste contexto, está sendo proposto um sistema de análise de eletrocardiograma como uma ferramenta de auxílio ao diagnóstico médico. O sistema classifica os batimentos de um registro de ECG ambulatorial tendo como referência o batimento predominante do paciente. A classificação se dá através de uma abordagem original não supervisionada que faz uso do método Alinhamento Temporal Dinâmico na comparação entre batimentos com tamanhos e formas diferentes. Além disso, é tratado neste trabalho o problema da classificação de batimentos prematuros a partir do estudo de rótulos feitos por cardiologistas nos batimentos da base utilizada neste trabalho. Por fim, é proposta uma interface gráfica que apresenta o resultado da análise realizada pelo sistema de classificação, destacando-se informações importantes e a morfologias dos batimentos predominantes ao longo de trechos do ECG. Os batimentos predominantes são determinados por um algoritmo original que realiza o cálculo do batimento médio a partir de um conjunto de batimentos. O sistema foi testado na MIT-BIH Arrhythmia Database e os resultados alcançados validaram a estratégia proposta.
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Maheswaran, Arulnesan. "A new Hilbert time warping principle for pattern matching /." Title page, contents and abstract only, 1985. http://web4.library.adelaide.edu.au/theses/09PH/09phm214.pdf.
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Wegner, Maus Victor, Gilberto Camara, Marius Appel, and Edzer Pebesma. "dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R." Foundation for Open Access Statistics, 2019. http://epub.wu.ac.at/6808/1/v88i05.pdf.
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The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.
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Dilmi, Mohamed Djallel. "Méthodes de classification des séries temporelles : application à un réseau de pluviomètres." Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS087.pdf.
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La question de l’impact du changement climatique sur l’évolution temporelle des précipitations ainsi que l’impact de l’ilot de chaleur parisien sur la répartition spatiale des précipitations motivent l’étude de la variabilité du cycle de l’eau à fine échelle en Île-de-France. Une façon d'analyser cette variabilité en utilisant les données d'un réseau de pluviomètres est d'effectuer une classification sur les séries temporelles mesurées par le réseau. Dans cette thèse, nous avons exploré deux approches pour la classification des séries temporelles : pour la première approche basée sur la description des séries par des caractéristiques, un algorithme de sélection des caractéristiques basé sur les algorithmes génétiques et les cartes topologiques a été proposé. Pour la deuxième approche basée sur la comparaison de formes, une mesure de dissimilarité (Itérative downscaling time warping) a été développée pour comparer deux séries temporelles. Ensuite les limites des deux approches ont été discutées et suivies d'une mise en place d'une approche mixte qui combine les avantages de chaque approche. L’approche a d’abord été appliquée à l’évaluation de la variabilité spatiale des précipitations. Pour l’évaluation de la variabilité temporelle des précipitations, une classification des événements de précipitation observés par une station a été réalisée puis étendue sur l’ensemble du réseau pluviométrique. L’application sur la série historique de Paris-Montsouris (1873-2015) permet de discriminer automatiquement les années « remarquables » d’un point de vue météorologiqueThe impact of climat change on the temporal evolution of precipitation as well as the impact of the Parisian heat island on the spatial distribution of précipitation motivate studying the varaibility of the water cycle on a small scale on île-de-france. one way to analyse this varaibility using the data from a rain gauge network is to perform a clustring on time series measured by this network. In this thesis, we have explored two approaches for time series clustring : for the first approach based on the description of series by characteristics, an algorithm for selecting characteristics based on genetic algorithms and topological maps has been proposed. for the second approach based on shape comparaison, a measure of dissimilarity (iterative downscaling time warping) was developed to compare two rainfall time series. Then the limits of the two approaches were discuddes followed by a proposition of a mixed approach that combine the advantages of each approach. The approach was first applied to the evaluation of spatial variability of precipitation on île-de-france. For the evaluation of the temporal variability of the precpitation, a clustring on the precipitation events observed by a station was carried out then extended on the whole rain gauge network. The application on the historical series of Paris-Montsouris (1873-2015) makes it possible to automatically discriminate "remarkable" years from a meteorological point of view
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Khan, Wasiq. "A Novel Approach for Continuous Speech Tracking and Dynamic Time Warping. Adaptive Framing Based Continuous Speech Similarity Measure and Dynamic Time Warping using Kalman Filter and Dynamic State Model." Thesis, University of Bradford, 2014. http://hdl.handle.net/10454/14802.
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Dynamic speech properties such as time warping, silence removal and background noise interference are the most challenging issues in continuous speech signal matching. Among all of them, the time warped speech signal matching is of great interest and has been a tough challenge for the researchers. An adaptive framing based continuous speech tracking and similarity measurement approach is introduced in this work following a comprehensive research conducted in the diverse areas of speech processing. A dynamic state model is introduced based on system of linear motion equations which models the input (test) speech signal frame as a unidirectional moving object along the template speech signal. The most similar corresponding frame position in the template speech is estimated which is fused with a feature based similarity observation and the noise variances using a Kalman filter. The Kalman filter provides the final estimated frame position in the template speech at current time which is further used for prediction of a new frame size for the next step. In addition, a keyword spotting approach is proposed by introducing wavelet decomposition based dynamic noise filter and combination of beliefs. The Dempster’s theory of belief combination is deployed for the first time in relation to keyword spotting task. Performances for both; speech tracking and keyword spotting approaches are evaluated using the statistical metrics and gold standards for the binary classification. Experimental results proved the superiority of the proposed approaches over the existing methods.
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He, Zhijun. "System And Algorithm Design For Varve Image Analysis System." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/196015.
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This dissertation describes the design and implementation of a computer vision based varve image analysis system. The primary issues covered are software engineering design, varve image calibrations, varve image enhancement, varve Dynamic Spatial Warping (DSW) profile generation, varve core image registration, varve identification, boundary identification and varve thickness measurement. A varve DSW matching algorithm is described to generate DSW profile and register two core images. Wavelet Multiple Resolution Analysis (MRA) is also used to do the core image registrations. By allowing an analyst to concentrate on other research work while the VARVES software analyzes a sample, much of the tedious varve analysis work is reduced, and potentially increasing the productivity. Additionally, by using new computer vision techniques, VARVES system is able to do some varve analysis which was impossible handled manually.
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Johnen, Benjamin [Verfasser]. "Bahnreferenzierung mittels Dynamic Time Warping zur Bewegungsanalyse von Industrierobotern / Benjamin Johnen." Aachen : Shaker, 2017. http://d-nb.info/1149269200/34.
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Correia, Maria Inês Costa. "Cluster analysis of financial time series." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21016.
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Mestrado em Mathematical FinanceEsta dissertação aplica o método da Signature como medida de similaridade entre dois objetos de séries temporais usando as propriedades de ordem 2 da Signature e aplicando-as a um método de Clustering Asimétrico. O método é comparado com uma abordagem de Clustering mais tradicional, onde a similaridade é medida usando Dynamic Time Warping, desenvolvido para trabalhar com séries temporais. O intuito é considerar a abordagem tradicional como benchmark e compará-la ao método da Signature através do tempo de computação, desempenho e algumas aplicações. Estes métodos são aplicados num conjunto de dados de séries temporais financeiras de Fundos Mútuos do Luxemburgo. Após a revisão da literatura, apresentamos o método Dynamic Time Warping e o método da Signature. Prossegue-se com a explicação das abordagens de Clustering Tradicional, nomeadamente k-Means, e Clustering Espectral Assimétrico, nomeadamente k-Axes, desenvolvido por Atev (2011). O último capítulo é dedicado à Investigação Prática onde os métodos anteriores são aplicados ao conjunto de dados. Os resultados confirmam que o método da Signature têm efectivamente potencial para Machine Learning e previsão, como sugerido por Levin, Lyons and Ni (2013).
This thesis applies the Signature method as a measurement of similarities between two time-series objects, using the Signature properties of order 2, and its application to Asymmetric Spectral Clustering. The method is compared with a more Traditional Clustering approach where similarities are measured using Dynamic Time Warping, developed to work with time-series data. The intention for this is to consider the traditional approach as a benchmark and compare it to the Signature method through computation times, performance, and applications. These methods are applied to a financial time series data set of Mutual Exchange Funds from Luxembourg. After the literature review, we introduce the Dynamic Time Warping method and the Signature method. We continue with the explanation of Traditional Clustering approaches, namely k-Means, and Asymmetric Clustering techniques, namely the k-Axes algorithm, developed by Atev (2011). The last chapter is dedicated to Practical Research where the previous methods are applied to the data set. Results confirm that the Signature method has indeed potential for machine learning and prediction, as suggested by Levin, Lyons, and Ni (2013).
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Baville, Paul. "Stratigraphic correlation uncertainty : On the impact of the sediment transport direction in computer-assisted multi-well correlation." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0111.
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La modélisation du sous-sol est un moyen de prédire la structure et la connectivité des unités stratigraphiques en honorant les observations de subsurface. Ces observations sont en général échantillonées le long de puits à grande échelle horizontale (kilomètre) mais à petite échelle verticale (mètre). Il y a deux types de données de puits : (1) les diagraphies, qui correspondent à des acquisitions géophysiques quasi-continus (échatillonage régulier) le long du puits (e.g., gamma ray, sonic, porosité neutron), et (2) les régions, qui correspondent à des propriétés réservoir discrètes définies par des profondeurs maximales et minimales le long du puits (e.g., biozones, zones structurales, faciès sédimentaires). Des marqueurs sont interprétés le long des puits et peuvent être associés pour générer un ensemble d'associations de marqueurs conformes, appelé des corrélations de puits. Ces corrélations de puits peuvent être réalisées manuellement (approche déterministe) par des experts, mais cela peut être sujet à des biais et ne garantit pas la reproductibilité. Les corrélations de puits peuvent également être générées automatiquement (approche déterministe ou probabiliste) en calculant à l'aide d'un algorithme un grand nombre de corrélations de puits conformes et en classant ces réalisations en fonction de leurs vraisemblances. La vraisemblance de ces corrélations de puits assistées par ordinateur est directement liée au principe de corrélation utilisé pour associer les marqueurs. Ces travaux de thèse introduisent deux principes de corrélation, qui tendent à reproduire la chronostratigraphie et les processus de dépôts à l'échelle de la paraséquence : (1) "un marqueur (décrit par un faciès et une distalité pris au centre d'un intervalle ayant un faciès constant et une distalité constante) ne peut pas être associé avec un autre marqueur décrit par un faciès plus profond à une position plus proximale, ou un faciès moins profond à une position plus distale", et (2) "plus la différence entre une interpolation chronostratigraphique (entre les marqueurs) et un profil de dépôt conceptuel est faible, plus la probabilité d'association des marqueurs est élevée". Ces deux principes de corrélation sont d'abord validés avec des solutions analytiques et appliqués sur des cas synthétiques. Ils ont ensuite été utilisés (1) pour prédire la connectivité des unités stratigraphiques à partir de données de puits sans connaissances solides sur les environnements de dépôt en inférant les paramètres de corrélation, ou (2) pour évaluer la probabilité d'un environnement de dépôt hypothétique en générant des réalisations stochastiques et en évaluant les incertitudes. Les methodes sont appliquées sur un système silicoclastique de dépôts deltaïques côtiers ciblant un réservoir du Jurassique Moyen dans le South Viking Graben en Mer du Nord. Ces travaux de thèse permettent (1) de définir deux principes de corrélation spécifiques définis par quelques paramètres qui peuvent être utilisés pour générer des corrélations de puits stochastiques dans les systèmes deltaïques côtiers, et (2) d'ouvrir la voie vers une combinaison simple de principes de corrélation spécifiques pour obtenir une meilleure caractérisation des systèmes deltaïques côtiers en évaluant les incertitudesSubsurface modeling is a way to predict the structure and the connectivity of stratigraphic units by honoring subsurface observations. These observations are commonly be sampled along wells at a large and sparse horizontal scale (kilometer-scale) but at a fine vertical scale (meter-scale). There are two types of well data: (1) well logs, corresponding to quasi-continuous (regular sampling) geophysical measurements along the well path (e.g., gamma ray, sonic, neutron porosity), and (2) regions, corresponding to categorical reservoir properties and defined by their top and bottom depths along the well path (e.g., biozones, structural zones, sedimentary facies). Markers are interpreted along the well path and can be associated in order to generate a consistent set of marker associations called well correlations. These well correlations may be generated manually (deterministic approach) by experts, but this may be prone to biases and does not ensure reproducibility. Well correlations may also be generated automatically (deterministic or probabilistic approach) by computing with an algorithm a large number of consistent well correlations and by ranking these realizations according to their likelihood. The likelihood of these computer-assisted well correlations are directly linked to the principle of correlation used to associate markers. This work introduces two principles of correlation, which tend to reproduce the chronostratigraphy and the depositional processes at the parasequence scale: (1) "a marker (described by facies and distality taken at the center of an interval having a constant facies and a constant distality) cannot be associated with another marker described by a depositionally deeper facies at a more proximal position, or a depositionally shallower facies at a more distal position", and (2) "the lower the difference between a chronostratigraphic interpolation (in between markers) and a conceptual depositional profile, the higher the likelihood of the marker association". These two principles of correlation are first benchmarked with analytical solutions and applied on synthetic cases. They have then been used (1) to predict the connectivity of stratigraphic units from well data without strong knowledge on depositional environments by inferring the correlation parameters, or (2) to evaluate the likelihood of a hypothetical depositional environment by generating stochastic realizations and assessing the uncertainties. The methods are applied on a siliciclastic coastal deltaic system targeting a Middle Jurassic reservoir in the South Viking Graben in the North Sea.This work enables (1) to define two specific principles of correlation defined by a few parameters that can be used to generate stochastically well correlations within coastal deltaic systems, and (2) to open the path towards a simple combination of specific principles of correlation to obtain a better characterization of coastal deltaic systems by assessing the uncertainties
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Silva, Diego Furtado. "Large scale similarity-based time series mining." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07122017-161346/.
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Time series are ubiquitous in the day-by-day of human beings. A diversity of application domains generate data arranged in time, such as medicine, biology, economics, and signal processing. Due to the great interest in time series, a large variety of methods for mining temporal data has been proposed in recent decades. Several of these methods have one characteristic in common: in their cores, there is a (dis)similarity function used to compare the time series. Dynamic Time Warping (DTW) is arguably the most relevant, studied and applied distance measure for time series analysis. The main drawback of DTW is its computational complexity. At the same time, there are a significant number of data mining tasks, such as motif discovery, which requires a quadratic number of distance computations. These tasks are time intensive even for less expensive distance measures, like the Euclidean Distance. This thesis focus on developing fast algorithms that allow large-scale analysis of temporal data, using similarity-based methods for time series data mining. The contributions of this work have implications in several data mining tasks, such as classification, clustering and motif discovery. Specifically, the main contributions of this thesis are the following: (i) an algorithm to speed up the exact DTW calculation and its embedding into the similarity search procedure; (ii) a novel DTW-based spurious prefix and suffix invariant distance; (iii) a music similarity representation with implications on several music mining tasks, and a fast algorithm to compute it, and; (iv) an efficient and anytime method to find motifs and discords under the proposed prefix and suffix invariant DTW.Séries temporais são ubíquas no dia-a-dia do ser humano. Dados organizados no tempo são gerados em uma infinidade de domínios de aplicação, como medicina, biologia, economia e processamento de sinais. Devido ao grande interesse nesse tipo de dados, diversos métodos de mineração de dados temporais foram propostos nas últimas décadas. Muitos desses métodos possuem uma característica em comum: em seu núcleo, há uma função de (dis)similaridade utilizada para comparar as séries. Dynamic Time Warping (DTW) é indiscutivelmente a medida de distância mais relevante na análise de séries temporais. A principal dificuldade em se utilizar a DTW é seu alto custo computacional. Ao mesmo tempo, algumas tarefas de mineração de séries temporais, como descoberta de motifs, requerem um alto número de cálculos de distância. Essas tarefas despendem um grande tempo de execução, mesmo utilizando-se medidas de distância menos custosas, como a distância Euclidiana. Esta tese se concentra no desenvolvimento de algoritmos eficientes que permitem a análise de dados temporais em larga escala, utilizando métodos baseados em similaridade. As contribuições desta tese têm implicações em variadas tarefas de mineração de dados, como classificação, agrupamento e descoberta de padrões frequentes. Especificamente, as principais contribuições desta tese são: (i) um algoritmo para acelerar o cálculo exato da distância DTW e sua incorporação ao processo de busca por similaridade; (ii) um novo algoritmo baseado em DTW para prover invariância a prefixos e sufixos espúrios no cálculo da distância; (iii) uma representação de similaridade musical com implicações em diferentes tarefas de mineração de dados musicais e um algoritmo eficiente para computá-la; (iv) um método eficiente e anytime para encontrar motifs e discords baseado na medida DTW invariante a prefixos e sufixos.
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Creaney-Stockton, Mary Jo. "Isolated word recognition using reduced connectivity neural networks with non-linear time alignment methods." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244333.
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Silva, Cassia Isac Gonçalves da. "Autenticacão de Assinaturas Online: Estudo dos Parâmetros do Dynamic Time Warping e da Representação da Assinatura." Universidade do Estado do Rio de Janeiro, 2011. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=6543.
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Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorO reconhecimento de padões é uma área da inteligência computacional que apoia a resolução de problemas utilizando ferramentas computacionais. Dentre esses problemas podem ser citados o reconhecimento de faces, a identificação de impressões digitais e a autenticação de assinaturas. A autenticação de assinaturas de forma automática tem sua relevância pois está ligada ao reconhecimento de indivíduos e suas credenciais em sistemas complexos e a questões financeiras. Neste trabalho é apresentado um estudo dos parâmetros do Dynamic Time Warping, um algoritmo utilizado para alinhar duas assinaturas e medir a similaridade existente entre elas. Variando-se os principais parâmetros desse algoritmo, sobre uma faixa ampla de valores, foram obtidas as médias dos resultados de erros na classificação, e assim, estas médias foram avaliadas. Com base nas primeiras avaliação, foi identificada a necessidade de se calcular um desses parâmetros de forma dinâmica, o gap cost, a fim de ajustá-lo no uso de uma aplicação prática. Uma proposta para a realização deste cálculo é apresentada e também avaliada. É também proposta e avaliada uma maneira alternativa de representação dos atributos da assinatura, de forma a considerar sua curvatura em cada ponto adquirido no processo de aquisição, utilizando os vetores normais como forma de representação. As avaliações realizadas durante as diversas etapas do estudo consideraram o Equal Error Rate (EER) como indicação de qualidade e as técnicas propostas foram comparadas com técnicas já estabelecidas, obtendo uma média percentual de EER de 3,47%.
Pattern recognition is an important aspect within the computational intelligence area, which helps solving problems that use computing tools. Among these problems we can cite face recognition, fingerprint identication and signature authentication. The relevance of automatic signature authentication is related to the recognition of an individual and his/her role in a complex system and it is often related to financial matters. This work presents a study of the Dynamic Time Warping parameters, which is an algorithm used to align two signatures and measure the similarity between them. In a first stage a set of experiments varied the main parameters of the algorithm in a broad range of values and the resulting averages of classification errors were evaluated. Based on these first evaluations the necessity to calculate dynamically one of these parameters, the gap cost,it was identified in order to adjust it for practical application. A proposal to calculate thisparameter is also presented and evaluated. It is also proposed and evaluated an alternative way to represent the signature attributes, considering the curvature at each point acquired in the acquisition process, using the normal vectors as a form of representation. The evaluations performed in the diverse stages of the study considered the Equal Error Rate (EER) as quality measure and the proposed techniques were compared to well-established ones, obtaining an average EER of 3.47 %.
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Dahlberg, Love. "Dynamic algorithm selection for machine learning on time series." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72576.
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We present a software that can dynamically determine what machine learning algorithm is best to use in a certain situation given predefined traits. The produced software uses ideal conditions to exemplify how such a solution could function. The software is designed to train a selection algorithm that can predict the behavior of the specified testing algorithms to derive which among them is the best. The software is used to summarize and evaluate a collection of selection algorithm predictions to determine which testing algorithm was the best during that entire period. The goal of this project is to provide a prediction evaluation software solution can lead towards a realistic implementation.
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Ahsan, Ramoza. "Time Series Data Analytics." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/529.
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Given the ubiquity of time series data, and the exponential growth of databases, there has recently been an explosion of interest in time series data mining. Finding similar trends and patterns among time series data is critical for many applications ranging from financial planning, weather forecasting, stock analysis to policy making. With time series being high-dimensional objects, detection of similar trends especially at the granularity of subsequences or among time series of different lengths and temporal misalignments incurs prohibitively high computation costs. Finding trends using non-metric correlation measures further compounds the complexity, as traditional pruning techniques cannot be directly applied. My dissertation addresses these challenges while meeting the need to achieve near real-time responsiveness. First, for retrieving exact similarity results using Lp-norm distances, we design a two-layered time series index for subsequence matching. Time series relationships are compactly organized in a directed acyclic graph embedded with similarity vectors capturing subsequence similarities. Powerful pruning strategies leveraging the graph structure greatly reduce the number of time series as well as subsequence comparisons, resulting in a several order of magnitude speed-up. Second, to support a rich diversity of correlation analytics operations, we compress time series into Euclidean-based clusters augmented by a compact overlay graph encoding correlation relationships. Such a framework supports a rich variety of operations including retrieving positive or negative correlations, self correlations and finding groups of correlated sequences. Third, to support flexible similarity specification using computationally expensive warped distance like Dynamic Time Warping we design data reduction strategies leveraging the inexpensive Euclidean distance with subsequent time warped matching on the reduced data. This facilitates the comparison of sequences of different lengths and with flexible alignment still within a few seconds of response time. Comprehensive experimental studies using real-world and synthetic datasets demonstrate the efficiency, effectiveness and quality of the results achieved by our proposed techniques as compared to the state-of-the-art methods.
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Petitjean, François. "Dynamic time warping : apports théoriques pour l'analyse de données temporelles : application à la classification de séries temporelles d'images satellites." Thesis, Strasbourg, 2012. http://www.theses.fr/2012STRAD023.
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Les séries temporelles d’images satellites (STIS) sont des données cruciales pour l’observation de la terre. Les séries temporelles actuelles sont soit des séries à haute résolution temporelle (Spot-Végétation, MODIS), soit des séries à haute résolution spatiale (Landsat). Dans les années à venir, les séries temporelles d’images satellites à hautes résolutions spatiale et temporelle vont être produites par le programme Sentinel de l’ESA. Afin de traiter efficacement ces immenses quantités de données qui vont être produites (par exemple, Sentinel-2 couvrira la surface de la terre tous les cinq jours, avec des résolutions spatiales allant de 10m à 60m et disposera de 13 bandes spectrales), de nouvelles méthodes ont besoin d’être développées. Cette thèse se focalise sur la comparaison des profils d’évolution radiométrique, et plus précisément la mesure de similarité « Dynamic Time Warping », qui constitue un outil permettant d’exploiter la structuration temporelle des séries d’images satellitesSatellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions, which aim at providing a coverage of the Earth every few days with high spatial resolution (ESA’s Sentinel program). In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling. In order to consistently handle the huge amount of information that will be produced (for instance, Sentinel-2 will cover the entire Earth’s surface every five days, with 10m to 60m spatial resolution and 13 spectral bands), new methods have to be developed. This Ph.D. thesis focuses on the “Dynamic Time Warping” similarity measure, which is able to take the most of the temporal structure of the data, in order to provide an efficient and relevant analysis of the remotely observed phenomena
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Niezen, Gerrit. "The optimization of gesture recognition techniques for resource-constrained devices." Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-01262009-125121/.
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Woo, Hyoungmin. "Development of Real-Time Predictive Analytics Tools for Small Water Distribution System." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504802657161527.
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Wang, Chiying. "Contributions to Collective Dynamical Clustering-Modeling of Discrete Time Series." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/198.
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The analysis of sequential data is important in business, science, and engineering, for tasks such as signal processing, user behavior mining, and commercial transactions analysis. In this dissertation, we build upon the Collective Dynamical Modeling and Clustering (CDMC) framework for discrete time series modeling, by making contributions to clustering initialization, dynamical modeling, and scaling.
We first propose a modified Dynamic Time Warping (DTW) approach for clustering initialization within CDMC. The proposed approach provides DTW metrics that penalize deviations of the warping path from the path of constant slope. This reduces over-warping, while retaining the efficiency advantages of global constraint approaches, and without relying on domain dependent constraints.
Second, we investigate the use of semi-Markov chains as dynamical models of temporal sequences in which state changes occur infrequently. Semi-Markov chains allow explicitly specifying the distribution of state visit durations. This makes them superior to traditional Markov chains, which implicitly assume an exponential state duration distribution.
Third, we consider convergence properties of the CDMC framework. We establish convergence by viewing CDMC from an Expectation Maximization (EM) perspective. We investigate the effect on the time to convergence of our efficient DTW-based initialization technique and selected dynamical models. We also explore the convergence implications of various stopping criteria.
Fourth, we consider scaling up CDMC to process big data, using Storm, an open source distributed real-time computation system that supports batch and distributed data processing.
We performed experimental evaluation on human sleep data and on user web navigation data. Our results demonstrate the superiority of the strategies introduced in this dissertation over state-of-the-art techniques in terms of modeling quality and efficiency.
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Lyubchyk, Leonid, Vladislav Kolbasin, and Galina Grinberg. "Nonlinear dynamic system kernel based reconstruction from time series data." Thesis, ТВіМС, 2015. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36826.
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A unified approach to reccurent kernel identification algorithms design is proposed. In order to fix the auxiliary vector dimension, the reduced order model kernel method is proposed and proper reccurent identification algorithms are designed.
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Guarienti, Gracyeli Santos Souza. "Desenvolvimento de uma técnica computacional de processamento espaço-temporal aplicada em séries de precipitação." Universidade Federal de Mato Grosso, 2015. http://ri.ufmt.br/handle/1/274.
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CAPES
Variáveis climatológicas podem ser estudadas a partir de seu comportamento temporal. Nesse sentido, este trabalho desenvolveu uma técnica computacional de processamento espaço-temporal de variáveis climatológicas que utiliza busca por similaridade e a possibilidade de comparação em várias resoluções temporais. Para demonstração do uso da técnica e verificação dos resultados, sequências de processamento foram aplicadas em séries de precipitação de um período de quinze anos usando os algoritmos Dynamic Time Warping (DTW) e wavelet em quatro biomas: Amazônia, Cerrado, Pantanal e Mata Atlântica. A técnica foi aplicada nas séries originais e em suas wavelets, com resoluções temporais mensal, semestral, anual e quinze anos de forma a permitir que análises específicas em cada resolução possam ser aplicadas. A flexibilidade e a variedade de resoluções temporais permitidas pela técnica torna possível acrescentar aos processos de monitoramento ambiental novas perspectivas em tomadas de decisão.
Climatic variables can be studied from its temporal behavior. In this sense, this study developed a temporal analysis technique for climatological variables using similarity search and the possibility of comparison in various temporal resolution levels. For the income statement, several processing sequences were applied in series of precipitation a period of fifteen years using the Dynamic Time Warping algorithm (DTW) and wavelet on four biomes: Amazon, Cerrado, Pantanal and Atlantic Forest. The technique was applied to the original data and wavelets, in the temporal resolution of time monthly, semi-annual, annual and fifteen years enable visualization and comparison of data on these different scales. Application the technique developed in this study, provide new perspectives to decision-making in environmental monitoring processes.
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Phan, Thi-Thu-Hong. "Elastic matching for classification and modelisation of incomplete time series." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0483/document.
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Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au VietnamMissing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam
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Carvalho, Thyago Peres. "Rastreamento e reconhecimento de movimentos de punho na execução de excertos musicais ao piano: uma abordagem com MD-DTW (Multi-Dimensional Dynamic Time Warping)." Universidade Federal de Goiás, 2015. http://repositorio.bc.ufg.br/tede/handle/tede/5234.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
This paper proposes a method to support the teaching-learning fist gestures in the piano music performance, using tracking and recognition of fist movements in executions of musical piano excerpts. For this, a system was built by means of computer vision techniques, aiming to present to the student videos produced to verify the execution of the exercise by the learner and aims to provide data related to performance. The system also uses the same computer vision techniques for the generation of the proposed exercises to class by the tutor in order to support the production of educational material as well. To recognize gestures, the system uses a regular low cost webcam, and from a colored marker on the back of the musician’s hand, the wrist movements are detected and tracked. A multidimensional dynamic time warping algorithm (MD-DTW) was used in order to develop this tool, which is an n-dimensional version of Dynamic Time Warping (DTW). In the work sequence, three rounds of experiments were performed, being the first of which to adjust the system parameters from video excerpts performed by an expert trainer. The second and third step assessed, respectively, the learning gain of piano students to the proposed method and system usability. The experiments were performed on volunteers with musical reading skills, however, without requiring minimum technical domain while playing the piano. The results of these tests showed that in addition to the method being able to detect and recognize successful gestures, the volunteers presented learning gain within middle range, which shows that this is a very promising method. In addition, usability testing revealed that the implemented interface, is well suited and has reached good satisfaction results among the volunteers. As a result, it can be said that the method and the proposed prototype demonstrate the potential of these tools in transferring techniques, such as musical performance gestures in a piano teachinglearning environment.
Este trabalho propõe um método de apoio ao ensino-aprendizagem de gestos de punho na execução musical ao piano, utilizando o rastreamento e reconhecimento de movimentos de punho na execução de excertos musicais ao piano. Para isso, um sistema foi construído, por meio de técnicas de visão computacional, visando apresentar ao aluno vídeos produzidos para verificar a execução do exercício pelo aprendiz, bem como visa fornecer dados relacionados ao desempenho. O sistema também utiliza as mesmas técnicas de visão computacional para a geração, pelo tutor, dos exercícios propostos para a aula, de modo a apoiar também na produção de material didático. Para reconhecer gestos, o sistema utiliza uma câmera regular de baixo custo, webcam, e, a partir de um marcador colorido no dorso da mão do músico, os movimentos de punho são detectados e rastreados. Para desenvolver essa ferramenta, foi utilizado o algoritmo Multidimensional Dynamic Time Warping (MD-DTW), que é uma versão n-dimensional do Dynamic Time Warping (DTW). Na sequência do trabalho, foram realizadas três etapas de experimentos, sendo que a primeira foi para ajustar os parâmetros do sistema a partir de vídeos dos excertos realizados por um instrutor especialista. A segunda e terceira etapa avaliam, respectivamente, o ganho de aprendizagem dos estudantes de piano com o método proposto e a usabilidade do sistema. Os experimentos foram realizados com voluntários com conhecimentos de leitura musical, porém, sem exigir limite mínimo de domínio de técnica ao tocar o piano. Os resultados desses testes mostraram que, além de o método ser capaz de detectar e reconhecer gestos com sucesso, os voluntários apresentaram ganho de aprendizagem na faixa média, o que demonstra ser esse um método bastante promissor. Além disso, o teste de usabilidade revelou que a interface implementada, é adequada e obteve bons resultados de satisfação entre os voluntários. Em virtude disso, pode-se afirmar que o método e o protótipo propostos demonstram o potencial dessas ferramentas no repasse de técnicas, como as de gestos de execução musical, em um ambiente de ensino-aprendizagem de piano.
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Caceres, Carlos Antonio. "Machine Learning Techniques for Gesture Recognition." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/52556.
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Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications.
In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field.
The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer.Master of Science
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Li, Yi-Huan, and 李易桓. "An Extended Dynamic Time Warping Algorithm and Architecture." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/75999984576298620586.
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碩士國立臺灣海洋大學
資訊工程學系
96
Abstract In the field of speech recognition, Dynamic Time Warping (DTW) algorithm has played an important role because of its advantage of overcoming the nonlinear expansion and contraction of the signals of speech recognition. In this thesis, we propose a new algorithm that can match three one-dimensional signals and solve the problem of the nonlinear expansion and contraction of the signals. Because this algorithm is computationally expensive, we wake the calculation by SIMD and systolic array structure, which are quite regular and only a few input and output are needed so that they are suitable to be applied in VLSI.
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Chen, Bo-Xian, and 陳柏憲. "A Fast Algorithm for Dynamic Time Warping with Adaptive Window." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/subw25.
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碩士國立中山大學
資訊工程學系研究所
106
The classification problem is a critical issue in data processing field, especially emph{time series classification} (TSC) problem. In the TSC problem, the calculation of the distance of two time series is the kernel issue. One of the famous methods for the distance calculation is the emph{dynamic time warping} (DTW), based on the dynamic programming. However, the time complexity of DTW is $O(n^2)$. When the data size is large, it takes too much time to calculate. In order to overcome time consuming problem, emph{dynamic time warping with window} (DTWW) combines the warping window into DTW calculation. This method reduces the computation time by restricting the number of possible solutions, so the answer of DTWW may not be the optimal solution. In this thesis, we present a method that expands the possible solutions in the minimum first order. Our method not only reduces the required computation time, but also gets the optimal answer.
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Teng, Hsin-Kai, and 鄧信凱. "Physical Fitness System Using Dynamic Time Warping Algorithm in Human Gesture Recognition." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/85576688311622366894.
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碩士慈濟大學
醫學資訊學系碩士班
102
A regular amount of exercise physical exercise can effectively maintain physical fitness and improve the overall health of elderly individuals. In spite of these benefits, it is particularly difficult for elderly individuals to maintain this physical activity due to their health problems. This study implementation of an in-home physical fitness exercise system by integrating motion capture technique and communication services, in order to encourage older adults to exercise and to maintain a physical activity routine. Our system consists of two modules, including medical healthcare platform and in-home physical fitness exercise system. Medical healthcare platform can collect the motion data from the patient for care givers to monitor the patients’ progress and to offer professional advice. The in-home physical fitness exercise system exploites the Microsoft's Kinect camera to perform real-time motion capture and to automatically analysis gesture parameters from the user’s movement. Using Dynamic Time Warping algorithm, the system can score the user’s exercise gestures by matching the detected movements to a set of reference models trained beforehand. To convenient the input process for elderly, the in-home physical fitness exercise system will uses a touch-based mobile device for users to wirelessly control the program interface. Furthermore, video communication will be introduced into the system, which offers visual and auditory interaction between users visually. In the experiment, Pearson's chi-squared test was used to compared the system to human rater and showed a p-value lower than 0.05, indicating a statistically significance on the correlation of the two method. The study ensures a high consistency with human rater and will be capable of assisting the elders for self-adjusting and improving when doing in-home exercises by using this system.
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Chang, Hsueh-Wei, and 張學瑋. "Nonintrusive Appliance Recognition Algorithm based on Ensemble Learning Model integrating with Dynamic Time Warping." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/35192290829923038200.
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碩士國立中興大學
資訊科學與工程學系
104
According to the research, if we can provide immediate and fine-grained power information to users, a significant reduction in the energy wastage can be achieved. Non-Intrusive Appliance Load Monitoring is an approach to reach the goal, which is more practical and feasible for typical families. In previous studies, we can discover that there were some disadvantages. First, it usually used high frequency sensor to acquire information, which made the cost of hardware higher. Second, most studies focused on the high consumption or on/off type appliances. As a result, low consumption appliances, multi-state appliances and continuously variable appliances were ignored. In this paper, we proposed a low cost and real-time approach. We use two-step detection in training phase and cluster detection in testing phase to confirm an event. Besides, we use a clustering algorithm-ISODATA to find an appropriate number of state for each appliance in the training set after feature extraction. Finally, we succeed to build the ensemble learning model integrating with dynamic time warping (DTW) model to identify appliances. Experimental results implies that two-step detection and cluster detection method can avoid excessive unknown appliance events, which can improve the accuracy of event detection. In addition, we can solve the problem of tie vote by using ensemble learning model integrating with DTW predictive model, which results in better recognition accuracy than using a single predictive model.
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Hu, Chiao-Feng, and 胡喬峰. "A Botnet Detection System Based on Signal Processing Technique and Dynamic Time Warping Algorithm." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/7b7p83.
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碩士國立交通大學
網路工程研究所
105
With the rapid development of network technology, network security has become a very important issue. Botnet has posed a great threat to cybersecurity in recent years. Therefore, there are a lot of botnet detection studies in decade. However, many of these studies rely on the packet size in a flow or the duration of a flow as features to distinguish whether a flow is a C&C communication of botnet. The attacker may easily evade these flow-based detection methods by changing the port, protocols or even the packet size. Hence, in this paper, we propose a conversation-based botnet detection system which use signal processing techniques and dynamic time warping algorithm. In the system, the packets will be aggregated into several conversations according to the source IP address and destination IP address. In this way, the port number and protocol will not affect. Besides, we calculate 6 new features based on Discrete Fourier Transform to view a conversation in the frequency domain. Finally, another 3K new features are calculated by using dynamic time warping algorithm. With these 6+3K features, we can improve the accuracy of which use the commonly used features in the past.
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Wang, Zhen (Jeff). "Minimum-risk sequence alignment for the alignment and recognition of action videos." Thesis, 2018. http://hdl.handle.net/10453/127933.
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University of Technology Sydney. Faculty of Engineering and Information Technology.Temporal alignment of videos is an important requirement of tasks such as video comparison, analysis and classification. In the context of action analysis and action recognition, the main guiding element for the temporal alignment are the human actions depicted in the videos. While well-established alignment algorithms such as dynamic time warping are available, they still heavily rely on basic linear cost models and heuristic parameter tuning. Inspired by the success of the hidden Markov support vector machine for pairwise alignment of protein sequences, in this thesis we present a novel framework which combines the flexibility of a pair hidden Markov model (PHMM) with the effective parameter training of the structural support vector machine (SSVM). The framework extends the scoring function of SSVM to capture the similarity of two input frame sequences and introduces suitable feature and loss functions. During learning, we leverage these loss functions for regularised empirical risk minimisation and effective parameter selection. We have carried out extensive experiments with the proposed technique (nicknamed as EHMM-SSVM) against state-of-the-art algorithms such as dynamic time warping (DTW) and generalized canonical time warping (GCTW) on pairs of human actions from four well-known datasets. The results show that the proposed model has been able to outperform the compared algorithms by a large margin in terms of alignment accuracy. In the second part of this thesis we employ our alignment approach to tackle the task of human action recognition in video. This task is highly challenging due to the substantial variations in motion performance, recording settings and inter-personal differences. Most current research focuses on the extraction of effective features and the design of suitable classifiers. Conversely, in this thesis we tackle this problem by a dissimilarity-based approach where classification is performed in terms of minimum distance from templates and where the distance is based on the score of our alignment model, the EHMM-SSVM. In turn, the templates are chosen by means of prototype selection techniques from the available samples of each class. Experimental results over two popular human action datasets have showed that the proposed approach has been capable of achieving an accuracy higher than many existing methods and comparable to a state-of-the-art action classification algorithm.
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Hsu, Che-jui, and 許哲睿. "Flexible Dynamic Time Warping for Time Series Classification." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/01609232819044102744.
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碩士國立中山大學
資訊工程學系研究所
103
Measuring the similarity or distance between two time series sequences is critical for the classification of a set of time series sequences. Given two time series sequences, X and Y, the dynamic time warping (DTW) algorithm can calculate the distance between X and Y. But the DTW algorithm may align some neighboring points in X to the corresponding points which are far apart in Y. This situation may cause that the alignment gets only a high alignment score, but it may lose its representative information. In this thesis, we propose the flexible dynamic time wrapping (FDTW) method for measuring the similarity of two time series sequences. Our algorithm adds an additional score as the reward for the long contiguous one-to-one segment. We also present the voting schemes and the behavior knowledge space (BKS) methods to construct classifier ensembles. As the experimental results show, our FDTW is indeed a crucial factor for improving the classification accuracy. The performance of a classifier ensemble, built by either voting or BKS, outperforms a single method.
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Cheng, Shin-Yi, and 鄭心怡. "A Dynamic Object Warping Algorithm for 3D Object Recognition." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/71170480371347087793.
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碩士國立臺灣海洋大學
資訊工程學系
95
Recognition of three-dimensional (3D) object is a very important task in computer vision. The traditional 3D object recognition method is using the projective invariant which can be derived from a complicated space geometric model. In this thesis, we propose a new Dynamic Object Warping (DOW) algorithm, which is a combination of Dynamic Space Warping (DSW) and Dynamic Time Warping (DTW) technologies. In the 3D object recognition, the DOW algorithm can be effective to solve some problems, like shrinking, enlarging, rotation, and distortion, etc. We also propose SIMD architecture for computing the DOW algorithm. Such architecture is well-suited for VLSI implementation because of regular structure and small number of input/output. Currently, the SIMD architecture has been designed on an FPGA and a VLSI chip for computing the DOW algorithm.
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Chen, Wei-Heng, and 陳維亨. "Wavefront Architecture for Computing the Dynamic Space Warping Algorithm." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28555142592508216218.
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碩士國立臺灣海洋大學
資訊工程學系
98
The Dynamic Space Warping (DSW) Algorithm is a dynamic program algorithm which has characters of compression and expansion, and can suitably apply to two-dimension image comparison. It can effective to solve the space warping problem in image recognition by its controllable warping distance, such as enlarging, shrinking, rotation, distortion, etc. By experiments, we indicates that the DSW has good recognition at w = n/10. For recognition and speed performance, we also explore the many different warping patterns and show the relationship between image’s size and warping distance by experimental. We also propose an SIMD-wavefront architecture to compute the DSW algorithm with diamond search, which is well-suited for Field Programmable Gate Array (FPGA) implementation because of the regular structure, the small number of input/output and the SIMD character.
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Ting, Chih-Hao, and 丁致豪. "VLSI Architecture for Computing the Dynamic Object Warping Algorithm." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/45466959564920598411.
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碩士國立臺灣海洋大學
資訊工程學系
99
In this thesis, we proposed VLSI architecture to compute the DOW Algorithm. The DOW Algorithm is a dynamic program algorithm which has characters of compression and expansion, and can suitably apply to three-dimension object comparison. It can effective to solve the space warping problem in object recognition by its controllable warping distance, such as enlarging, shrinking, rotation, distortion, etc. The algorithm is contains a structure in six-dimensional complexity, to be aimed at the estimated problems which need massively to operate and slow, An ASIC(Application Specific Integrated Circuit) chip is very helpful to increase computation potency of the recognition system. We also propose SIMD architecture for computing the DOW algorithm. Such architecture is well-suited for VLSI implementation because of regular structure and small number of input/output. The SIMD architecture has been Simulation and Verification.
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LU, HONG-PING, and 呂弘屏. "Time Series Classification by Dynamic Time Warping with Compressed Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nm9gt6.
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碩士國立高雄大學
統計學研究所
106
This study proposes an algorithm combining the dynamic time warping (DTW) and compressed learning (CL) techniques for time series classification. The DTW is used to address nonsynchronous effects in multiple time series for determining an adequate reference trajectory. The CL is employed to classify the time series efficiently by cooperating with the reference trajectory. By applying the proposed algorithm and three other methods to several data sets, the proposed algorithm is shown to have satisfactory classification accuracies within a reasonable time. In addition, the proposed algorithm is extended to establish an online monitoring system to detect different types of arrhythmia. The numerical results indicate that the online system is capable of obtaining accurate detection results.
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Diab, D. M., B. AsSadhan, H. Binsalleeh, S. Lambotharan, K. G. Kyriakopoulos, and Ibrahim Ghafir. "Denial of service detection using dynamic time warping." 2021. http://hdl.handle.net/10454/18458.
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YesWith the rapid growth of security threats in computer networks, the need for developing efficient security‐warning systems is substantially increasing. Distributed denial‐of‐service (DDoS) and DoS attacks are still among the most effective and dreadful attacks that require robust detection. In this work, we propose a new method to detect TCP DoS/DDoS attacks. Since analyzing network traffic is a promising approach, our proposed method utilizes network traffic by decomposing the TCP traffic into control and data planes and exploiting the dynamic time warping (DTW) algorithm for aligning these two planes with respect to the minimum Euclidean distance. By demonstrating that the distance between the control and data planes is considerably small for benign traffic, we exploit this characteristic for detecting attacks as outliers. An adaptive thresholding scheme is implemented by adjusting the value of the threshold in accordance with the local statistics of the median absolute deviation (MAD) of the distances between the two planes. We demonstrate the efficacy of the proposed method for detecting DoS/DDoS attacks by analyzing traffic data obtained from publicly available datasets.
The Deanship of Scientific Research, King Saud University. The Gulf Science, Innovation, and Knowledge Economy Programme of the U.K. Government
The full-text of this article will be released for public view at the end of the publisher embargo on 12 Apr 2022.
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Wu, Chun-Lung, and 吳俊龍. "A VLSI Architecture for Computing the Dynamic Space Warping Algorithm." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/06349418935688126482.
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碩士國立臺灣海洋大學
資訊工程學系
96
Dynamic Space Warping (DSW) algorithm, a two-dimensional image recognition method with good recognition ability, contains a structure in four-dimensional complexity. In the fields of image processing and image recognizing, massive and fast computation are urgent needed. To be aimed at the estimated problems which need massively to operate and slow, an ASIC (Application Specific Integrated Circuit) chip is very helpful to increase computation potency of the recognition system. In this thesis, we propose a VLSI architecture to compute the DSW Algorithm, which is well-suited for VLSI implementation because of the regular structure, the small number of input/output and the SIMD (Single Instruction stream Multiple Data stream) character.
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張文謙. "A data path chip for the dynamic space warping algorithm." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/41343710596138294841.
Full text
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Nair, Nishanth Ulhas. "Joint Evaluation Of Multiple Speech Patterns For Speech Recognition And Training." Thesis, 2009. https://etd.iisc.ac.in/handle/2005/630.
Full textAbstract:
Improving speech recognition performance in the presence of noise and interference continues to be a challenging problem. Automatic Speech Recognition (ASR) systems work well when the test and training conditions match. In real world environments there is often a mismatch between testing and training conditions. Various factors like additive noise, acoustic echo, and speaker accent, affect the speech recognition performance. Since ASR is a statistical pattern recognition problem, if the test patterns are unlike anything used to train the models, errors are bound to occur, due to feature vector mismatch. Various approaches to robustness have been proposed in the ASR literature contributing to mainly two topics: (i) reducing the variability in the feature vectors or (ii) modify the statistical model parameters to suit the noisy condition. While some of those techniques are quite effective, we would like to examine robustness from a different perspective. Considering the analogy of human communication over telephones, it is quite common to ask the person speaking to us, to repeat certain portions of their speech, because we don't understand it. This happens more often in the presence of background noise where the intelligibility of speech is affected significantly. Although exact nature of how humans decode multiple repetitions of speech is not known, it is quite possible that we use the combined knowledge of the multiple utterances and decode the unclear part of speech. Majority of ASR algorithms do not address this issue, except in very specific issues such as pronunciation modeling. We recognize that under very high noise conditions or bursty error channels, such as in packet communication where packets get dropped, it would be beneficial to take the approach of repeated utterances for robust ASR. In this thesis, we have formulated a set of algorithms for both joint evaluation/decoding for recognizing noisy test utterances as well as utilize the same formulation for selective training of Hidden Markov Models (HMMs), again for robust performance.
We first address joint recognition of multiple speech patterns given that they belong to the same class. We formulated this problem considering the patterns as isolated words. If there are K test patterns (K ≥ 2) of a word by a speaker, we show that it is possible to improve the speech recognition accuracy over independent single pattern evaluation of test speech, for the case of both clean and noisy speech. We also find the state sequence which best represents the K patterns. This formulation can be extended to connected word recognition or continuous speech recognition also.
Next, we consider the benefits of joint multi-pattern likelihood for HMM training. In the usual HMM training, all the training data is utilized to arrive at a best possible parametric model. But, it is possible that the training data is not all genuine and therefore may have labeling errors, noise corruptions, or plain outlier exemplars. Such outliers will result in poorer models and affect speech recognition performance. So it is important to selectively train them so that the outliers get a lesser weightage. Giving lesser weight to an entire outlier pattern has been addressed before in speech recognition literature. However, it is possible that only some portions of a training pattern are corrupted. So it is important that only the corrupted portions of speech are given a lesser weight during HMM training and not the entire pattern. Since in HMM training, multiple patterns of speech from each class are used, we show that it is possible to use joint evaluation methods to selectively train HMMs such that only the corrupted portions of speech are given a lesser weight and not the entire speech pattern.
Thus, we have addressed all the three main tasks of a HMM, to jointly utilize the availability of multiple patterns belonging to the same class. We experimented the new algorithms for Isolated Word Recognition in the case of both clean speech and noisy speech. Significant improvement in speech recognition performance is obtained, especially for speech affected by transient/burst noise.
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Nair, Nishanth Ulhas. "Joint Evaluation Of Multiple Speech Patterns For Speech Recognition And Training." Thesis, 2009. http://hdl.handle.net/2005/630.
Full textAbstract:
Improving speech recognition performance in the presence of noise and interference continues to be a challenging problem. Automatic Speech Recognition (ASR) systems work well when the test and training conditions match. In real world environments there is often a mismatch between testing and training conditions. Various factors like additive noise, acoustic echo, and speaker accent, affect the speech recognition performance. Since ASR is a statistical pattern recognition problem, if the test patterns are unlike anything used to train the models, errors are bound to occur, due to feature vector mismatch. Various approaches to robustness have been proposed in the ASR literature contributing to mainly two topics: (i) reducing the variability in the feature vectors or (ii) modify the statistical model parameters to suit the noisy condition. While some of those techniques are quite effective, we would like to examine robustness from a different perspective. Considering the analogy of human communication over telephones, it is quite common to ask the person speaking to us, to repeat certain portions of their speech, because we don't understand it. This happens more often in the presence of background noise where the intelligibility of speech is affected significantly. Although exact nature of how humans decode multiple repetitions of speech is not known, it is quite possible that we use the combined knowledge of the multiple utterances and decode the unclear part of speech. Majority of ASR algorithms do not address this issue, except in very specific issues such as pronunciation modeling. We recognize that under very high noise conditions or bursty error channels, such as in packet communication where packets get dropped, it would be beneficial to take the approach of repeated utterances for robust ASR. In this thesis, we have formulated a set of algorithms for both joint evaluation/decoding for recognizing noisy test utterances as well as utilize the same formulation for selective training of Hidden Markov Models (HMMs), again for robust performance.
We first address joint recognition of multiple speech patterns given that they belong to the same class. We formulated this problem considering the patterns as isolated words. If there are K test patterns (K ≥ 2) of a word by a speaker, we show that it is possible to improve the speech recognition accuracy over independent single pattern evaluation of test speech, for the case of both clean and noisy speech. We also find the state sequence which best represents the K patterns. This formulation can be extended to connected word recognition or continuous speech recognition also.
Next, we consider the benefits of joint multi-pattern likelihood for HMM training. In the usual HMM training, all the training data is utilized to arrive at a best possible parametric model. But, it is possible that the training data is not all genuine and therefore may have labeling errors, noise corruptions, or plain outlier exemplars. Such outliers will result in poorer models and affect speech recognition performance. So it is important to selectively train them so that the outliers get a lesser weightage. Giving lesser weight to an entire outlier pattern has been addressed before in speech recognition literature. However, it is possible that only some portions of a training pattern are corrupted. So it is important that only the corrupted portions of speech are given a lesser weight during HMM training and not the entire pattern. Since in HMM training, multiple patterns of speech from each class are used, we show that it is possible to use joint evaluation methods to selectively train HMMs such that only the corrupted portions of speech are given a lesser weight and not the entire speech pattern.
Thus, we have addressed all the three main tasks of a HMM, to jointly utilize the availability of multiple patterns belonging to the same class. We experimented the new algorithms for Isolated Word Recognition in the case of both clean speech and noisy speech. Significant improvement in speech recognition performance is obtained, especially for speech affected by transient/burst noise.
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Μπόρας, Ιωσήφ. "Αυτόματος τεμαχισμός ψηφιακών σημάτων ομιλίας και εφαρμογή στη σύνθεση ομιλίας, αναγνώριση ομιλίας και αναγνώριση γλώσσας." Thesis, 2009. http://nemertes.lis.upatras.gr/jspui/handle/10889/2068.
Full textAbstract:
Η παρούσα διατριβή εισάγει μεθόδους για τον αυτόματο τεμαχισμό σημάτων ομιλίας. Συγκεκριμένα παρουσιάζονται τέσσερις νέες μέθοδοι για τον αυτόματο τεμαχισμό σημάτων ομιλίας, τόσο για γλωσσολογικά περιορισμένα όσο και μη προβλήματα. Η πρώτη μέθοδος κάνει χρήση των σημείων του σήματος που αντιστοιχούν στα ανοίγματα των φωνητικών χορδών κατά την διάρκεια της ομιλίας για να εξάγει όρια ψευδό-φωνημάτων με χρήση του αλγορίθμου δυναμικής παραμόρφωσης χρόνου. Η δεύτερη τεχνική εισάγει μια καινοτόμα υβριδική μέθοδο εκπαίδευσης κρυμμένων μοντέλων Μαρκώφ, η οποία τα καθιστά πιο αποτελεσματικά στον τεμαχισμό της ομιλίας. Η τρίτη μέθοδος χρησιμοποιεί αλγορίθμους μαθηματικής παλινδρόμησης για τον συνδυασμό ανεξαρτήτων μηχανών τεμαχισμού ομιλίας. Η τέταρτη μέθοδος εισάγει μια επέκταση του αλγορίθμου Βιτέρμπι με χρήση πολλαπλών παραμετρικών τεχνικών για τον τεμαχισμό της ομιλίας. Τέλος, οι προτεινόμενες μέθοδοι τεμαχισμού χρησιμοποιούνται για την βελτίωση συστημάτων στο πρόβλημα της σύνθεσης ομιλίας, αναγνώρισης ομιλίας και αναγνώρισης γλώσσας.The present dissertation introduces methods for the automatic segmentation of speech signals. In detail, four new segmentation methods are presented both in for the cases of linguistically constrained or not segmentation. The first method uses pitchmark points to extract pseudo-phonetic boundaries using dynamic time warping algorithm. The second technique introduces a new hybrid method for the training of hidden Markov models, which makes them more effective in the speech segmentation task. The third method uses regression algorithms for the fusion of independent segmentation engines. The fourth method is an extension of the Viterbi algorithm using multiple speech parameterization techniques for segmentation. Finally, the proposed methods are used to improve systems in the task of speech synthesis, speech recognition and language recognition.
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黃致巽. "Dynamic Time Warping Based Recognition Of Human Body Gestures." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/06953692238370152202.
Full textAbstract:
碩士國立臺灣師範大學
資訊工程學系
101
Recognition technology is a very important issue in the area of computer vision. The applications include fingerprint recognition, iris recognition and gestures recognition in our lives. Body gestures are composed of many continuously static poses that are segmented from the action, it’s difficult to work because high complexity information by using common webcam. In recent years, somatosensory system had become popular in gestures recognition, user could control the object in the screen like using mouse or keyboard. This paper establishes a human body gestures recognition system, which can recognized the gestures from user defined. Using Kinect to generated 3D coordinates value of skeleton joints, and using translation method to normalized coordinates, after that, we get a new original point by Shoulder Center point. We use Dynamic Time Warping (DTW) algorithm and 1-Nearest Neighbor to compare and classify body gestures. Calculating the Euclidean Distance between training data and testing data. The minimum distance is the result. Finally, we design an Incremental method to keep recognition rate without any extra training by user. In our system, the average recognition rate of static gestures in general sample is 86.02%, five gestures defined by user is 75.60%, and increasing almost 3% after using incremental method.