Gotowe bibliografie tematyczne / Audio data mining

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  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
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Gotowa bibliografia na temat „Audio data mining”

Autor: Grafiati
Data publikacji: 29 lipca 2024
Data aktualizacji: 31 lipca 2024

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Artykuły w czasopismach na temat "Audio data mining"

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Xu, Shasha. "Effective Graph Mining for Educational Data Mining and Interest Recommendation". Applied Bionics and Biomechanics 2022 (12.08.2022): 1–5. http://dx.doi.org/10.1155/2022/7610124.

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In order to fully understand and analyze the rules and cognitive characteristics of users’ learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users’ learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users’ resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester.
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Xu, Yanping, i Sen Xu. "A Clustering Analysis Method for Massive Music Data". Modern Electronic Technology 5, nr 1 (6.05.2021): 24. http://dx.doi.org/10.26549/met.v5i1.6763.

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Clustering analysis plays a very important role in the field of data mining, image segmentation and pattern recognition. The method of cluster analysis is introduced to analyze NetEYun music data. In addition, different types of music data are clustered to find the commonness among the same kind of music. A music data-oriented clustering analysis method is proposed: Firstly, the audio beat period is calculated by reading the audio file data, and the emotional features of the audio are extracted; Secondly, the audio beat period is calculated by Fourier transform. Finally, a clustering algorithm is designed to obtain the clustering results of music data.
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THURAISINGHAM, BHAVANI. "MANAGING AND MINING MULTIMEDIA DATABASES". International Journal on Artificial Intelligence Tools 13, nr 03 (wrzesień 2004): 739–59. http://dx.doi.org/10.1142/s0218213004001776.

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Several advances have been made on managing multimedia databases as well as on data mining. Recently there is active research on mining multimedia databases. This paper provides an overview of managing multimedia databases and then describes issues on mining multimedia databases. In particular mining text, image, audio and video data are discussed.
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Wang, Fang. "The Effect of Multimedia Teaching Model of Music Course in Colleges and Universities Based on Classroom Audio Data Mining Technology". Tobacco Regulatory Science 7, nr 5 (30.09.2021): 4520–31. http://dx.doi.org/10.18001/trs.7.5.2.18.

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Objectives: With the rapid development of information technology, multimedia teaching mode carries a large amount of audio-visual information, quickly occupies the music classroom in Colleges and universities, and becomes the mainstream teaching mode of music teaching in Colleges and universities. Methods: Based on this, this study uses classroom audio data mining technology to analyze the effect of multimedia teaching mode of music courses in Colleges and universities. The method of audio data mining is analyzed in college music multimedia classroom. The advanced embedded SOPC system is used to decode the MP3 audio files played in music courses by combining software and hardware. The performance of the multimedia teaching system in college music courses is optimized. Results: The hardware resources are made use of the flexibility of SOPC (System-on-a-Programmable-Chip) system. Reasonable allocation achieves the optimal design of teaching mode. Finally, the superiority of the algorithm is verified by testing. The test results show that the decoding speed and efficiency of audio files can be significantly improved by combining hardware and software. Conclusion: At the same time, the system has greater flexibility and expandable space, which can effectively promote the multimedia teaching effect of music courses in Colleges and universities. The research in this paper is helpful to the flexible transformation of multimedia teaching mode of music courses in Colleges and universities, and provides an important reference for the popularization of multimedia and the wide use of data mining technology in music courses in Colleges and universities.
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Paul, Prantosh K., i K. S. Shivraj. "Multimedia Data Mining and its Integration in Information Sector and Foundation: An Overview". Asian Journal of Computer Science and Technology 3, nr 1 (5.05.2014): 24–28. http://dx.doi.org/10.51983/ajcst-2014.3.1.1729.

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Information and Communication Technologies are one of the important component and toll. Virtually, the advent of Electronic resources and similar foundation use in Information Foundation and similar foundation has brought about significant changes in storage and communication of information. Data mining process consist of several process and stages, which are related to each other and interactive. This is the way of mining or extraction of data from the Database or Dataset. Extraction of Data with multimedia nature such as audio, video, images, text may be called as Multimedia Data Mining. In Information Foundation, Data Mining has wonderful role and importance. This paper is talks about Multimedia Information and Data Mining and its characteristics. Paper also talks about role and need of Multimedia Data Mining in Information and Similar Foundation.
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Ye, Jiaxing, Takumi Kobayashi, Xiaoyan Wang, Hiroshi Tsuda i Masahiro Murakawa. "Audio Data Mining for Anthropogenic Disaster Identification: An Automatic Taxonomy Approach". IEEE Transactions on Emerging Topics in Computing 8, nr 1 (1.01.2020): 126–36. http://dx.doi.org/10.1109/tetc.2017.2700843.

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Li, Xaiomeng. "Construction of Teachers Performance Evaluation Index System for Data-Driven Smart Classrooms in Secondary Schools". SHS Web of Conferences 190 (2024): 03010. http://dx.doi.org/10.1051/shsconf/202419003010.

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Smart classroom is a new teaching paradigm for the digital transformation of education, which utilizes methods such as audio and video intelligent recognition, model construction, and data mining to evaluate teaching effectiveness and quality, in order to achieve automatic and full process evaluation and feedback of teacher teaching quality. This article is based on the massive real-time audio and video data generated by smart classrooms. By mining the hidden patterns and values of educational and teaching data, and using the Delphi method to construct a data-driven performance evaluation index system for secondary schools smart classroom teachers, it can fully reflect the real performance of secondary schools teachers in the smart classroom, achieving a comprehensive, all staff, fair, and objective evaluation of secondary schools teachers, overcoming the shortcomings of traditional evaluation methods.
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Shin, Sanghyun, Abhishek Vaidya i Inseok Hwang. "Helicopter Cockpit Audio Data Analysis to Infer Flight State Information". Journal of the American Helicopter Society 65, nr 3 (1.07.2020): 1–8. http://dx.doi.org/10.4050/jahs.65.032001.

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In recent years, the National Transportation Safety Board has highlighted the importance of analyzing flight data as one of the effective methods to improve the safety and efficiency of helicopter operations. Since cockpit audio data contain various sounds from engines, alarms, crew conversations, and other sources within a cockpit, analyzing cockpit audio data can help identify the causes of incidents and accidents. Among the various types of the sounds in cockpit audio data, this paper focuses on cockpit alarm and engine sounds as an object of analysis. This paper proposes cockpit audio analysis algorithms, which can detect types and occurrence times of alarm sounds for an abnormal flight and estimate engine-related flight parameters such as an engine torque. This is achieved by the following: for alarm sound analysis, finding the highest correlation with the short time Fourier transform, and the Cumulative Sum Control Chart (CUSUM) using a database of the characteristic features of the alarm; and for engine sound analysis, using data mining and statistical modeling techniques to identify specific frequencies associated with engine operations. The proposed algorithm is successfully applied to a set of simulated audio data, which were generated by the X-plane flight simulator, and real audio data, which were recorded by GoPro cameras in Sikorsky S-76 helicopters to demonstrate its desired performance.
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Faridzi, Salman Al, Faza Shafa Azizah, Faizal Mustafa, Azzahra Nindya Putri, Gilang Ramadhika, Fauzan Rizky Aditya, Ridha Sherli Fadilah i in. "PENGOLAHAN DATA: PEMAHAMAN GEMPA BUMI DI INDONESIA MELALUI PENDEKATAN DATA MINING". Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS 2, nr 1 (16.02.2024): 262–70. http://dx.doi.org/10.59407/jpki2.v2i1.506.

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Gempa bumi merupakan bencana alam yang sering terjadi di Indonesia akibat interaksi lempeng tektonik. Indonesia terletak pada pertemuan empat lempeng tektonik dunia, yang menyebabkan aktivitas zona tumbukan dan patahan yang berpotensi memicu gempa bumi. Meskipun telah terjadi sejumlah peristiwa gempa bumi besar di Indonesia, prediksi gempa secara tepat waktu masih sulit karena kompleksitas geologi dan dinamika kerak bumi. Peningkatan pemahaman tentang perilaku geologi dan sistem peringatan dini menjadi kunci dalam mempersiapkan diri menghadapi ancaman gempa bumi di masa mendatang. Data mining adalah proses yang berguna untuk mengeksplorasi dan mencari nilai informasi kompleks yang tersimpan dalam basis data. Dengan menggunakan data mining, dampak atau akibat dari gempa bumi yang terjadi di Indonesia dapat dipelajari berdasarkan data gempa bumi yang telah terjadi sebelumnya. Maka, dilakukanlah webinar dan workshop tentang penggunaan data mining untuk memahami pola gempa bumi di Indonesia selama 10 tahun terakhir. Webinar membahas dasar-dasar data mining dan fakta gempa yang terjadi di Indonesia, sementara workshop membahas pengolahan dan visualisasi data gempa bumi menggunakan bahasa Python dan Google Colab. Workshop ini terbatas pada pengolahan dan visualisasi data csv gempa bumi saja. Kegiatan webinar dan workshop dilaksanakan pada tanggal 29 Januari 2024 pukul 13.00 WIB. Hasil evaluasi menunjukkan bahwa peserta menyatakan kepuasan mereka terhadap acara tersebut, dengan sebagian besar peserta memberikan nilai positif terhadap penyampaian materi, kesesuaian materi dengan tema, kejelasan informasi, serta kualitas audio visual selama acara berlangsung.
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Bhoyar, Sanjay, Punam Bhoyar, Anuj Kumar i Prabha Kiran. "Enhancing applications of surveillance through multimedia data mining". Journal of Discrete Mathematical Sciences and Cryptography 27, nr 3 (2024): 1105–20. http://dx.doi.org/10.47974/jdmsc-1947.

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Over recent years, multimedia data has become a cornerstone for insightful data analysis, yielding vital information crucial for informed decision-making processes. This diverse data format encompasses audio, video, images, and text, offering a wealth of valuable knowledge. Advancements in multimedia acquisition, storage, and processing technologies have significantly enhanced analytical capabilities, overcoming challenges posed by semi-structured and unstructured data formats. Various entities including corporations, governmental bodies, and academic institutions are keenly interested in harnessing insights from the vast reservoirs of multimedia data generated across diverse sources. Consequently, researchers have delved into data mining methodologies, uncovering effective strategies for extracting insights from multimedia datasets. This study aims to probe the conceptual and practical dimensions of multimedia data mining within surveillance contexts, elucidating its transformative impact on diverse sectors by facilitating efficient data collection, analysis, and dissemination processes. Moreover, it underscores the significance of incorporating relevant cryptography methods to bolster the system’s integrity and completeness.
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Rozprawy doktorskie na temat "Audio data mining"

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Levy, Marcel Andrew. "Ringermute an audio data mining toolkit /". abstract and full text PDF (free order & download UNR users only), 2005. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433402.

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Kohlsdorf, Daniel. "Data mining in large audio collections of dolphin signals". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53968.

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The study of dolphin cognition involves intensive research of animal vocal- izations recorded in the field. In this dissertation I address the automated analysis of audible dolphin communication. I propose a system called the signal imager that automatically discovers patterns in dolphin signals. These patterns are invariant to frequency shifts and time warping transformations. The discovery algorithm is based on feature learning and unsupervised time series segmentation using hidden Markov models. Researchers can inspect the patterns visually and interactively run com- parative statistics between the distribution of dolphin signals in different behavioral contexts. The required statistics for the comparison describe dolphin communication as a combination of the following models: a bag-of-words model, an n-gram model and an algorithm to learn a set of regular expressions. Furthermore, the system can use the patterns to automatically tag dolphin signals with behavior annotations. My results indicate that the signal imager provides meaningful patterns to the marine biologist and that the comparative statistics are aligned with the biologists’ domain knowledge.
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Thambiratnam, Albert J. K. "Acoustic keyword spotting in speech with applications to data mining". Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/37254/1/Albert_Thambiratnam_Thesis.pdf.

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Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.
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Fenet, Sébastien. "Empreintes audio et stratégies d'indexation associées pour l'identification audio à grande échelle". Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0051/document.

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Dans cet ouvrage, nous définissons précisément ce qu’est l’identification audio à grande échelle. En particulier, nous faisons une distinction entre l’identification exacte, destinée à rapprocher deux extraits sonores provenant d’un même enregistrement, et l’identification approchée, qui gère également la similarité musicale entre les signaux. A la lumière de ces définitions, nous concevons et examinons plusieurs modèles d’empreinte audio et évaluons leurs performances, tant en identification exacte qu’en identificationapprochée
N this work we give a precise definition of large scale audio identification. In particular, we make a distinction between exact and approximate matching. In the first case, the goal is to match two signals coming from one same recording with different post-processings. In the second case, the goal is to match two signals that are musically similar. In light of these definitions, we conceive and evaluate different audio-fingerprint models
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Fenet, Sébastien. "Empreintes audio et stratégies d'indexation associées pour l'identification audio à grande échelle". Electronic Thesis or Diss., Paris, ENST, 2013. http://www.theses.fr/2013ENST0051.

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Dans cet ouvrage, nous définissons précisément ce qu’est l’identification audio à grande échelle. En particulier, nous faisons une distinction entre l’identification exacte, destinée à rapprocher deux extraits sonores provenant d’un même enregistrement, et l’identification approchée, qui gère également la similarité musicale entre les signaux. A la lumière de ces définitions, nous concevons et examinons plusieurs modèles d’empreinte audio et évaluons leurs performances, tant en identification exacte qu’en identificationapprochée
N this work we give a precise definition of large scale audio identification. In particular, we make a distinction between exact and approximate matching. In the first case, the goal is to match two signals coming from one same recording with different post-processings. In the second case, the goal is to match two signals that are musically similar. In light of these definitions, we conceive and evaluate different audio-fingerprint models
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Ferroudj, Meriem. "Detection of rain in acoustic recordings of the environment using machine learning techniques". Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/82848/1/Meriem_Ferroudj_Thesis.pdf.

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This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.
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Bayle, Yann. "Apprentissage automatique de caractéristiques audio : application à la génération de listes de lecture thématiques". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0087/document.

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Ce mémoire de thèse de doctorat présente, discute et propose des outils de fouille automatique de mégadonnées dans un contexte de classification supervisée musical.L'application principale concerne la classification automatique des thèmes musicaux afin de générer des listes de lecture thématiques.Le premier chapitre introduit les différents contextes et concepts autour des mégadonnées musicales et de leur consommation.Le deuxième chapitre s'attelle à la description des bases de données musicales existantes dans le cadre d'expériences académiques d'analyse audio.Ce chapitre introduit notamment les problématiques concernant la variété et les proportions inégales des thèmes contenus dans une base, qui demeurent complexes à prendre en compte dans une classification supervisée.Le troisième chapitre explique l'importance de l'extraction et du développement de caractéristiques audio et musicales pertinentes afin de mieux décrire le contenu des éléments contenus dans ces bases de données.Ce chapitre explique plusieurs phénomènes psychoacoustiques et utilise des techniques de traitement du signal sonore afin de calculer des caractéristiques audio.De nouvelles méthodes d'agrégation de caractéristiques audio locales sont proposées afin d'améliorer la classification des morceaux.Le quatrième chapitre décrit l'utilisation des caractéristiques musicales extraites afin de trier les morceaux par thèmes et donc de permettre les recommandations musicales et la génération automatique de listes de lecture thématiques homogènes.Cette partie implique l'utilisation d'algorithmes d'apprentissage automatique afin de réaliser des tâches de classification musicale.Les contributions de ce mémoire sont résumées dans le cinquième chapitre qui propose également des perspectives de recherche dans l'apprentissage automatique et l'extraction de caractéristiques audio multi-échelles
This doctoral dissertation presents, discusses and proposes tools for the automatic information retrieval in big musical databases.The main application is the supervised classification of musical themes to generate thematic playlists.The first chapter introduces the different contexts and concepts around big musical databases and their consumption.The second chapter focuses on the description of existing music databases as part of academic experiments in audio analysis.This chapter notably introduces issues concerning the variety and unequal proportions of the themes contained in a database, which remain complex to take into account in supervised classification.The third chapter explains the importance of extracting and developing relevant audio features in order to better describe the content of music tracks in these databases.This chapter explains several psychoacoustic phenomena and uses sound signal processing techniques to compute audio features.New methods of aggregating local audio features are proposed to improve song classification.The fourth chapter describes the use of the extracted audio features in order to sort the songs by themes and thus to allow the musical recommendations and the automatic generation of homogeneous thematic playlists.This part involves the use of machine learning algorithms to perform music classification tasks.The contributions of this dissertation are summarized in the fifth chapter which also proposes research perspectives in machine learning and extraction of multi-scale audio features
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Wallace, Roy Geoffrey. "Fast and accurate phonetic spoken term detection". Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/39610/1/Roy_Wallace_Thesis.pdf.

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For the first time in human history, large volumes of spoken audio are being broadcast, made available on the internet, archived, and monitored for surveillance every day. New technologies are urgently required to unlock these vast and powerful stores of information. Spoken Term Detection (STD) systems provide access to speech collections by detecting individual occurrences of specified search terms. The aim of this work is to develop improved STD solutions based on phonetic indexing. In particular, this work aims to develop phonetic STD systems for applications that require open-vocabulary search, fast indexing and search speeds, and accurate term detection. Within this scope, novel contributions are made within two research themes, that is, accommodating phone recognition errors and, secondly, modelling uncertainty with probabilistic scores. A state-of-the-art Dynamic Match Lattice Spotting (DMLS) system is used to address the problem of accommodating phone recognition errors with approximate phone sequence matching. Extensive experimentation on the use of DMLS is carried out and a number of novel enhancements are developed that provide for faster indexing, faster search, and improved accuracy. Firstly, a novel comparison of methods for deriving a phone error cost model is presented to improve STD accuracy, resulting in up to a 33% improvement in the Figure of Merit. A method is also presented for drastically increasing the speed of DMLS search by at least an order of magnitude with no loss in search accuracy. An investigation is then presented of the effects of increasing indexing speed for DMLS, by using simpler modelling during phone decoding, with results highlighting the trade-off between indexing speed, search speed and search accuracy. The Figure of Merit is further improved by up to 25% using a novel proposal to utilise word-level language modelling during DMLS indexing. Analysis shows that this use of language modelling can, however, be unhelpful or even disadvantageous for terms with a very low language model probability. The DMLS approach to STD involves generating an index of phone sequences using phone recognition. An alternative approach to phonetic STD is also investigated that instead indexes probabilistic acoustic scores in the form of a posterior-feature matrix. A state-of-the-art system is described and its use for STD is explored through several experiments on spontaneous conversational telephone speech. A novel technique and framework is proposed for discriminatively training such a system to directly maximise the Figure of Merit. This results in a 13% improvement in the Figure of Merit on held-out data. The framework is also found to be particularly useful for index compression in conjunction with the proposed optimisation technique, providing for a substantial index compression factor in addition to an overall gain in the Figure of Merit. These contributions significantly advance the state-of-the-art in phonetic STD, by improving the utility of such systems in a wide range of applications.
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Ziegler, Thomas. "Auswertung von Audit-Daten zur Optimierung von Workflows". [S.l. : s.n.], 2001. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB9386075.

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Wang, Tian. "Effective Thermal Resistance of Commercial Buildings Using Data Analysis of Whole-Building Electricity Data". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586524438396894.

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Książki na temat "Audio data mining"

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Baronas, Roberto, i Maria Inês Pagliarini Cox. Linguística popular: Folk linguistics : práticas, proposições e polêmicas - homenagem a Amadeu Amaral. Campinas, SP: Pontes, 2020.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Multimedia information extraction: Advances in video, audio, and imagery analysis for search, data mining, surveillance, and authoring. Hoboken, N.J: Wiley, 2012.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.

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Maybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. Wiley & Sons, Limited, John, 2012.

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Financial management: Fiscal year 1992 audit of the Defense Cooperation Account : report to the Congress. Washington, D.C: The Office, 1993.

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Części książek na temat "Audio data mining"

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Sai Tharun, A., K. Dhivakar i R. Nair Prashant. "Voice Data-Mining on Audio from Audio and Video Clips". W Smart Innovation, Systems and Technologies, 519–34. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7447-2_46.

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Zheng, Meizhen, Peng Bai i Xiaodong Shi. "A Compact Phoneme-To-Audio Aligner for Singing Voice". W Advanced Data Mining and Applications, 183–97. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_13.

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Nguyen, Cong Phuong, Ngoc Yen Pham i Eric Castelli. "First Steps to an Audio Ontology-Based Classifier for Telemedicine". W Advanced Data Mining and Applications, 845–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_92.

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Liu, Qingzhong, Andrew H. Sung i Mengyu Qiao. "Spectrum Steganalysis of WAV Audio Streams". W Machine Learning and Data Mining in Pattern Recognition, 582–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_44.

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Bansal, Mohit, Satya Jeet Raj Upali i Sukesha Sharma. "Early Parkinson Disease Detection Using Audio Signal Processing". W Emerging Technologies in Data Mining and Information Security, 243–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4193-1_23.

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Kubera, Elżbieta, i Alicja A. Wieczorkowska. "Mining Audio Data for Multiple Instrument Recognition in Classical Music". W New Frontiers in Mining Complex Patterns, 246–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_16.

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Gao, Jie, Yanqing Sun, Hongbin Suo, Qingwei Zhao i Yonghong Yan. "WAPS: An Audio Program Surveillance System for Large Scale Web Data Stream". W Web Information Systems and Mining, 116–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05250-7_13.

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Park, Dong-Chul, Yunsik Lee i Dong-Min Woo. "Classification of Audio Signals Using a Bhattacharyya Kernel-Based Centroid Neural Network". W Advances in Knowledge Discovery and Data Mining, 604–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_59.

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Das, Sanghamitra, Suchibrota Dutta, Debanjan Banerjee i Arijit Ghosal. "Classification of Bharatnatyam and Kathak Dance Form Through Audio Signal". W Emerging Technologies in Data Mining and Information Security, 671–79. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9774-9_62.

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Muhammad, Atta, i Sher Muhammad Daudpota. "Content Based Identification of Talk Show Videos Using Audio Visual Features". W Machine Learning and Data Mining in Pattern Recognition, 267–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41920-6_20.

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Streszczenia konferencji na temat "Audio data mining"

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Tao, Yudong, Samantha G. Mitsven, Lynn K. Perry, Daniel S. Messinger i Mei-Ling Shyu. "Audio-Based Group Detection for Classroom Dynamics Analysis". W 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00125.

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"Improvement of Indexing Methods for Audio Fingerprinting Systems". W International Conference Data Mining, Civil and Mechanical Engineering. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0214053.

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Rodrigues, João Pedro, i Emerson Paraiso. "From audio to information: Learning topics from audio transcripts". W Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11967.

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Streszczenie:
In this work, the technical feasibility of working with audio transcriptions from Youtube is analyzed, as well as presenting a method that allows data acquisition, pre-processing, and post-processing to work with this type of data. A topic modeling approach with the latent dirichlet allocation algorithm is used. An approach is also presented to dynamically determine the ideal number of topics that make up a given corpus. In the experiments, a database of 250 audio transcriptions was used, obtaining a model with coherence in the range of 40%.
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Hao, Yuan, Mohammad Shokoohi-Yekta, George Papageorgiou i Eamonn Keogh. "Parameter-Free Audio Motif Discovery in Large Data Archives". W 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.30.

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Shen, Jiaxing, Oren Lederman, Jiannong Cao, Florian Berg, Shaojie Tang i Alex Pentland. "GINA: Group Gender Identification Using Privacy-Sensitive Audio Data". W 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00061.

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S. N. Lagmiri i H. Bakhous. "AUDIO ENCRYPTION ALGORITHM USING HYPERCHAOTIC SYSTEMS OF DIFFERENT DIMENSIONS". W 3rd International Conference on Data Mining & Knowledge Management. AIRCC Publication Corporation, 2018. http://dx.doi.org/10.5121/csit.2018.81507.

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Ebrahimi, Samaneh, Hossein Vahabi, Matthew Prockup i Oriol Nieto. "Predicting Audio Advertisement Quality". W WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159652.3159701.

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Yu, Lingyun, Jun Yu i Qiang Ling. "Mining Audio, Text and Visual Information for Talking Face Generation". W 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00089.

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Sageder, Gerhard, Maia Zaharieva i Matthias Zeppelzauer. "Unsupervised Selection of Robust Audio Feature Subsets". W Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.79.

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Chu, Eric, i Deb Roy. "Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies". W 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.100.

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Raporty organizacyjne na temat "Audio data mining"

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Craven, J. A., G. McNeice, B. Powell, R. Koch, I R Annesley, G. Wood i J. Mwenifumbo. First look at data from a three-dimensional audio-magnetotelluric survey at the McArthur River mining camp, northern Saskatchewan. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214207.

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Jajodia, Sushi. Integration of Audit Data Analysis and Mining Techniques into Aide. Fort Belvoir, VA: Defense Technical Information Center, lipiec 2006. http://dx.doi.org/10.21236/ada456840.

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