Academic literature on the topic 'Time in music Data processing'

Abstract An important preprocessing step for several music signal processing algorithms is the estimation of playing instruments in music recordings. To this aim, time-dependent instrument recognition is realized by a neural network with residual blocks in this approach. Since music signal processing tasks use diverse time-frequency representations as input matrices, the influence of different input representations for instrument recognition is analyzed in this work. Three-dimensional inputs of short-time Fourier transform (STFT) magnitudes and an additional time-frequency representation based on phase information are investigated as well as two-dimensional STFT or constant-Q transform (CQT) magnitudes. As additional phase representations, the product spectrum (PS), based on the modified group delay, and the frequency error (FE) matrix, related to the instantaneous frequency, are used. Training and evaluation processes are executed based on the MusicNet dataset, which enables the estimation of seven instruments. With a higher number of frequency bins in the input representations, an improved instrument recognition of about 2 % in F1-score can be achieved. Compared to the literature, frame-level instrument recognition can be improved for different input representations.

In this paper, principal-singular-vector utilization for modal analysis (PUMA) was adapted to perform time delay estimation on ground-penetrating radar (GPR) data by taking into account the shape of the transmitted GPR signal. The super-resolution capability of PUMA was used to separate overlapping backscattered echoes from a layered pavement structure with some embedded debondings. The well-known root-MUSIC algorithm was selected as a benchmark for performance assessment. The simulation results showed that the proposed PUMA performs very well, especially in the case where the sources are totally coherent, and it requires much less computational time than the root-MUSIC algorithm.

With the continuous development of information technology and the arrival of the era of big data, music appreciation has also entered the digital development. Big data essence is highlighted by comparison with traditional data management and processing technologies. Under different requirements, the required time processing range is different. Music appreciation is an essential and important part of music lessons, which can enrich people’s emotional experience, improve aesthetic ability, and cultivate noble sentiments. Data processing of music information resources will greatly facilitate the management, dissemination, and big data analysis and processing of music resources and improve the ability of music lovers to appreciate music. This paper aims to study the digital development of music in the environment of big data, making music appreciation more convenient and intelligent. This paper proposes an intelligent music recognition and appreciation model based on deep neural network (DNN) model. The use of DNN allows this study to have significant improvement over the traditional algorithm. This paper proposes an intelligent music recognition and appreciation model based on the DNN model and improves the traditional algorithm. The improved method in this paper refers to the Dropout method on the traditional DNN model. The DNN is trained on the database and tested on the data. The results show that, in the same database, the traditional DNN model is 114 and the RNN model is 120. The PPL of the improved DNN model in this paper is 98, i.e., the lowest value. The convergence speed is faster, which indicates that the model has stronger music recognition ability and it is more conducive to the digital development of music appreciation.

Abstract The development of music is a tortuous process, and the network relationship between each genre and each artist is intricate. In order to have a better understanding of the history of music, this paper tells the stories hidden in the history of music by means of data processing. Firstly, this paper establishes a model to evaluate the similarity between music by using ISOMAP algorithm. At the same time, the forest evolution model was established to mark the most revolutionary musical characters. Finally, using the Page-Rank algorithm, we get the founders of several music genres. It turns out that the figures who led the development of music don’t coincide with the figures who revolutionized music. Through the analysis of this paper, we can more clearly understand the development of music and the evolution of genres.

Musical data mining covers a number of methodologies to successfully apply data mining techniques for music processing, drawing together a multidisciplinary array of top experts. The field of music data acquisition has grown through time to solve the difficulties of obtaining and engaging with enormous amounts of music and associated data, such as styles, artists, lyrics, and reviews. In order to improve the quality of music teaching, a music teaching evaluation based on data mining is proposed. Data mining is becoming more widely accepted as a viable form of inquiry for analyzing data obtained in natural settings. More and more attention is paid to music teaching. Actual data is frequently inadequate, unreliable, and/or lacking in specific behaviors or patterns, as well as including numerous inaccuracies. Preprocessing data is a tried-and-true means of resolving such problems. Music teaching data is divided into three steps after preprocessing, that is, “object and object type,” “music teaching data normalization,” and “data integration.” A model is built with a high-dimensional characteristic distribution and essential parameters of convergent teaching capacity. The experimental results show that the data mining method can be used for music teaching evaluation and has the advantages of short evaluation time, high accuracy, and clear indicators.

The Music Therapy Checklist is useful for music therapists to monitor and evaluate the music therapeutic process. A list of different types of behaviors were selected based on results derived from applying the Music Therapy Coding Scheme. The use of a checklist to code the events with a recording method based on 1-min. intervals allows observation without data-processing systems and drastically reduces coding time. At the same time, the checklist tags the main factors in musical interaction.

Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second.

This paper starts with the basic process of music recognition to complete the study on extraction and realization of seven musical characteristics of the music features characterization, at the same time, the paper in-depth studies the pitch value duration, tonality characteristic extraction unit. Fourier analysis method based on short-time uses the computer programming for audio signal automatic analysis and processing, implements the characteristics recognition of the piano music playing, Experimental data show that the average recognition rate of algorithm is above 95% with the strong recognition ability, which provides the core technology support for developing the evaluation system of piano performance.

Computer music environments (CMEs) are notoriously difficult to design and implement. As computer programs, they reflect the complex nature of music ontology and must support real-time manipulation of multimedia data. In addition, these programs must be usable by native users, supporting their creative process without obstructing it through technical difficulties. To achieve these goals, the authors argue, CMEs must be provided with a well-defined methodology relying on techniques from the fields of software engineering, artificial intelligence, and knowledge representation. This paper contributes an aspect of this methodology, concentrating on the role of visualizations in CMEs. The authors state that visualization deserves a specialized theory that is based on music ontology and that is independent of the concrete, implemented graphical interface.

This article examines the nature and time course of the processing of discontinuous dependency relationships in language and draws suggestive parallels to similar issues in music perception. The on-line language comprehension data presented demonstrate that discontinuous structural dependencies cause reactivation of the misordered or "stranded" sentential material at its underlying canonical position in the sentence during ongoing comprehension. Further, this process is demonstrated to be driven by structural knowledge, independent of pragmatic information, aided by prosodic cues, and dependent on rate of input. Issues of methodology and of theory that are equally relevant to language and music are detailed.

A powerful and efficient algorithm has been designed to deal with the critical timing problem of the MIDI messages. This algorithm can convert note events stored in a natural way to MIDI messages dynamically. Only limited memory space (the buffer) is required to finish the conversion work, and the size of the buffer is independent of the size of the original sequence (notes). This algorithm's real-time variable properties suggest not only the flexible real-time controls in the use of musical aspects, but also the expandability to interactive multi-media applications. A compositional environment called MusicSculptor has been implemented in terms of this algorithm.

This paper discusses several compositions that use the computer screen to present music notation to performers. Three of these compositions, Law of Fives (2015), Polytera II (2016), and Terraformation (2016–17), employ strategies that allow the notation to change during the performance of the work as the product of composer-regulated algorithmic generation and performer interaction. New methodologies, implemented using Cycling74's Max software, facilitate performance of these works by allowing effective control of generation and on-screen display of notation; these include an application called VizScore, which delivers notation and conducts through it in real-time, and a development environment for real-time notation using the Bach extensions and graphical overlays around them. These tools support a concept of cartographic composition, in which a composer maps a range of potential behaviors that are mediated by human or algorithmic systems or some combination of the two. Notational variation in performance relies on computer algorithms that can both generate novel ideas and be subject to formal plans designed by the composer. This requires a broader discussion of the underlying algorithms and control mechanisms in the context of algorithmic art in general. Terraformation, for viola and computer, uses a model of the performer's physical actions to constrain the algorithmic generation of musical material displayed in on-screen notation. The resulting action-based on-screen notation system combines common practice notation with fingerboard tablature, color gradients, and abstract graphics. This hybrid model of dynamic notation puts unconventional demands on the performer; implications of this new performance practice are addressed, including behaviors, challenges, and freedoms of real-time notation.

This work focuses on the automatic identification of musical piece versions (alternate renditions of the same musical composition like cover songs, live recordings, remixes, etc.). In particular, we propose two core approaches for version identification: model-free and model-based ones. Furthermore, we introduce the use of post-processing strategies to improve the identification of versions. For all that we employ nonlinear signal analysis tools and concepts, complex networks, and time series models. Overall, our work brings automatic version identification to an unprecedented stage where high accuracies are achieved and, at the same time, explores promising directions for future research. Although our steps are guided by the nature of the considered signals (music recordings) and the characteristics of the task at hand (version identification), we believe our methodology can be easily transferred to other contexts and domains.
Aquest treball es centra en la identificació automàtica de versions musicals (interpretacions alternatives d'una mateixa composició: 'covers', directes, remixos, etc.). En concret, proposem dos tiupus d'estratègies: la lliure de model i la basada en models. També introduïm tècniques de post-processat per tal de millorar la identificació de versions. Per fer tot això emprem conceptes relacionats amb l'anàlisi no linial de senyals, xarxes complexes i models de sèries temporals. En general, el nostre treball porta la identificació automàtica de versions a un estadi sense precedents on s'obtenen bons resultats i, al mateix temps, explora noves direccions de futur. Malgrat que els passos que seguim estan guiats per la natura dels senyals involucrats (enregistraments musicals) i les característiques de la tasca que volem solucionar (identificació de versions), creiem que la nostra metodologia es pot transferir fàcilment a altres àmbits i contextos.

Sensor it is the most powerful part of any system. Aviation industry is the plase where milions of sensors is be used for difetrent purpuses. Othe wery important task of avionics equipment is data transfer between sensors to processing equipment. Why it is so important to transmit data online into MatLab? Nowadays rapidly are developing unmanned aerial vehicles. If we can transmit data from UAV sensors into MatLab, then we can process it and get the desired information about UAV. Of course we have to use the most chipiest way to data transfer. Today everyone in the world has mobile phone. Many of them has different sensors, such as: pressure sensor, temperature sensor, gravity sensor, gyroscope, rotation vector sensor, proximity sensor, light sensor, orientation sensor, magnetic field sensor, accelerometer, GPS receiver and so on. It will be cool if we can use real time data from cell phone sensors for some navigation tasks. In our work we use mobile phone Samsung Galaxy SIII with all sensors which are listed above except temperature sensor. There are existing many programs for reading and displaying data from sensors, such as: “Sensor Kinetics”, “Sensors”, “Data Recording”, “Android Sensors Viewer”. We used “Data Recording”. For the purpose of transmitting data from cell phone there are following methods: - GPRS (Mobile internet); - Bluetooth; - USB cable; - Wi-Fi. After comparing this methods we analyzed that GPRS is uncomfortable for us because we should pay for it, Bluetooth has small coverage, USB cable has not such portability as others methods. So we decided that Wi-Fi is optimal method on transmitting data for our goal

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California
The Telemetry Data Center (TDC) at White Sands Missile Range (WSMR) is now beginning to modernize its existing telemetry data processing system. Modern networking and interactive graphical displays are now being introduced. This infusion of modern technology will allow the TDC to provide our customers with enhanced data processing and display capability. The intent of this project is to outline this undertaking.

International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California
The U.S. Army Aviation Development Test Activity at Fort Rucker, Alabama needed a real-time test data collection and processing capability for helicopter flight testing. The system had to be capable of collecting and processing both FM and PCM data streams from analog tape and/or a telemetry receiver. The hardware and software was to be off the shelf whenever possible. The integration was to result in a stand alone telemetry collection and processing system.

This thesis addresses several shortcomings on the current state of the art methodologies in music information retrieval (MIR). In particular, it proposes several computational approaches to automatically analyze and describe music scores and audio recordings of Ottoman-Turkish makam music (OTMM). The main contributions of the thesis are the music corpus that has been created to carry out the research and the audio-score alignment methodology developed for the analysis of the corpus. In addition, several novel computational analysis methodologies are presented in the context of common MIR tasks of relevance for OTMM. Some example tasks are predominant melody extraction, tonic identification, tempo estimation, makam recognition, tuning analysis, structural analysis and melodic progression analysis. These methodologies become a part of a complete system called Dunya-makam for the exploration of large corpora of OTMM. The thesis starts by presenting the created CompMusic Ottoman- Turkish makam music corpus. The corpus includes 2200 music scores, more than 6500 audio recordings, and accompanying metadata. The data has been collected, annotated and curated with the help of music experts. Using criteria such as completeness, coverage and quality, we validate the corpus and show its research potential. In fact, our corpus is the largest and most representative resource of OTMM that can be used for computational research. Several test datasets have also been created from the corpus to develop and evaluate the specific methodologies proposed for different computational tasks addressed in the thesis. The part focusing on the analysis of music scores is centered on phrase and section level structural analysis. Phrase boundaries are automatically identified using an existing state-of-the-art segmentation methodology. Section boundaries are extracted using heuristics specific to the formatting of the music scores. Subsequently, a novel method based on graph analysis is used to establish similarities across these structural elements in terms of melody and lyrics, and to label the relations semiotically. The audio analysis section of the thesis reviews the state-of-the-art for analysing the melodic aspects of performances of OTMM. It proposes adaptations of existing predominant melody extraction methods tailored to OTMM. It also presents improvements over pitch-distribution-based tonic identification and makam recognition methodologies. The audio-score alignment methodology is the core of the thesis. It addresses the culture-specific challenges posed by the musical characteristics, music theory related representations and oral praxis of OTMM. Based on several techniques such as subsequence dynamic time warping, Hough transform and variable-length Markov models, the audio-score alignment methodology is designed to handle the structural differences between music scores and audio recordings. The method is robust to the presence of non-notated melodic expressions, tempo deviations within the music performances, and differences in tonic and tuning. The methodology utilizes the outputs of the score and audio analysis, and links the audio and the symbolic data. In addition, the alignment methodology is used to obtain score-informed description of audio recordings. The scoreinformed audio analysis not only simplifies the audio feature extraction steps that would require sophisticated audio processing approaches, but also substantially improves the performance compared with results obtained from the state-of-the-art methods solely relying on audio data. The analysis methodologies presented in the thesis are applied to the CompMusic Ottoman-Turkish makam music corpus and integrated into a web application aimed at culture-aware music discovery. Some of the methodologies have already been applied to other music traditions such as Hindustani, Carnatic and Greek music. Following open research best practices, all the created data, software tools and analysis results are openly available. The methodologies, the tools and the corpus itself provide vast opportunities for future research in many fields such as music information retrieval, computational musicology and music education.
Esta tesis aborda varias limitaciones de las metodologías más avanzadas en el campo de recuperación de información musical (MIR por sus siglas en inglés). En particular, propone varios métodos computacionales para el análisis y la descripción automáticas de partituras y grabaciones de audio de música de makam turco-otomana (MMTO). Las principales contribuciones de la tesis son el corpus de música que ha sido creado para el desarrollo de la investigación y la metodología para alineamiento de audio y partitura desarrollada para el análisis del corpus. Además, se presentan varias metodologías nuevas para análisis computacional en el contexto de las tareas comunes de MIR que son relevantes para MMTO. Algunas de estas tareas son, por ejemplo, extracción de la melodía predominante, identificación de la tónica, estimación de tempo, reconocimiento de makam, análisis de afinación, análisis estructural y análisis de progresión melódica. Estas metodologías constituyen las partes de un sistema completo para la exploración de grandes corpus de MMTO llamado Dunya-makam. La tesis comienza presentando el corpus de música de makam turcootomana de CompMusic. El corpus incluye 2200 partituras, más de 6500 grabaciones de audio, y los metadatos correspondientes. Los datos han sido recopilados, anotados y revisados con la ayuda de expertos. Utilizando criterios como compleción, cobertura y calidad, validamos el corpus y mostramos su potencial para investigación. De hecho, nuestro corpus constituye el recurso de mayor tamaño y representatividad disponible para la investigación computacional de MMTO. Varios conjuntos de datos para experimentación han sido igualmente creados a partir del corpus, con el fin de desarrollar y evaluar las metodologías específicas propuestas para las diferentes tareas computacionales abordadas en la tesis. La parte dedicada al análisis de las partituras se centra en el análisis estructural a nivel de sección y de frase. Los márgenes de frase son identificados automáticamente usando uno de los métodos de segmentación existentes más avanzados. Los márgenes de sección son extraídos usando una heurística específica al formato de las partituras. A continuación, se emplea un método de nueva creación basado en análisis gráfico para establecer similitudes a través de estos elementos estructurales en cuanto a melodía y letra, así como para etiquetar relaciones semióticamente. La sección de análisis de audio de la tesis repasa el estado de la cuestión en cuanto a análisis de los aspectos melódicos en grabaciones de MMTO. Se proponen modificaciones de métodos existentes para extracción de melodía predominante para ajustarlas a MMTO. También se presentan mejoras de metodologías tanto para identificación de tónica basadas en distribución de alturas, como para reconocimiento de makam. La metodología para alineación de audio y partitura constituye el grueso de la tesis. Aborda los retos específicos de esta cultura según vienen determinados por las características musicales, las representaciones relacionadas con la teoría musical y la praxis oral de MMTO. Basada en varias técnicas tales como deformaciones dinámicas de tiempo subsecuentes, transformada de Hough y modelos de Markov de longitud variable, la metodología de alineamiento de audio y partitura está diseñada para tratar las diferencias estructurales entre partituras y grabaciones de audio. El método es robusto a la presencia de expresiones melódicas no anotadas, desviaciones de tiempo en las grabaciones, y diferencias de tónica y afinación. La metodología utiliza los resultados del análisis de partitura y audio para enlazar el audio y los datos simbólicos. Además, la metodología de alineación se usa para obtener una descripción informada por partitura de las grabaciones de audio. El análisis de audio informado por partitura no sólo simplifica los pasos para la extracción de características de audio que de otro modo requerirían sofisticados métodos de procesado de audio, sino que también mejora sustancialmente su rendimiento en comparación con los resultados obtenidos por los métodos más avanzados basados únicamente en datos de audio. Las metodologías analíticas presentadas en la tesis son aplicadas al corpus de música de makam turco-otomana de CompMusic e integradas en una aplicación web dedicada al descubrimiento culturalmente específico de música. Algunas de las metodologías ya han sido aplicadas a otras tradiciones musicales, como música indostaní, carnática y griega. Siguiendo las mejores prácticas de investigación en abierto, todos los datos creados, las herramientas de software y los resultados de análisis está disponibles públicamente. Las metodologías, las herramientas y el corpus en sí mismo ofrecen grandes oportunidades para investigaciones futuras en muchos campos tales como recuperación de información musical, musicología computacional y educación musical.
Aquesta tesi adreça diverses deficiències en l’estat actual de les metodologies d’extracció d’informació de música (Music Information Retrieval o MIR). En particular, la tesi proposa diverses estratègies per analitzar i descriure automàticament partitures musicals i enregistraments d’actuacions musicals de música Makam Turca Otomana (OTMM en les seves sigles en anglès). Les contribucions principals de la tesi són els corpus musicals que s’han creat en el context de la tesi per tal de dur a terme la recerca i la metodologia de alineament d’àudio amb la partitura que s’ha desenvolupat per tal d’analitzar els corpus. A més la tesi presenta diverses noves metodologies d’anàlisi computacional d’OTMM per a les tasques més habituals en MIR. Alguns exemples d’aquestes tasques són la extracció de la melodia principal, la identificació del to musical, l’estimació de tempo, el reconeixement de Makam, l’anàlisi de la afinació, l’anàlisi de la estructura musical i l’anàlisi de la progressió melòdica. Aquest seguit de metodologies formen part del sistema Dunya-makam per a la exploració de grans corpus musicals d’OTMM. En primer lloc, la tesi presenta el corpus CompMusic Ottoman- Turkish makam music. Aquest inclou 2200 partitures musicals, més de 6500 enregistraments d’àudio i metadata complementària. Les dades han sigut recopilades i anotades amb ajuda d’experts en aquest repertori musical. El corpus ha estat validat en termes de d’exhaustivitat, cobertura i qualitat i mostrem aquí el seu potencial per a la recerca. De fet, aquest corpus és el la font més gran i representativa de OTMM que pot ser utilitzada per recerca computacional. També s’han desenvolupat diversos subconjunts de dades per al desenvolupament i evaluació de les metodologies específiques proposades per a les diverses tasques computacionals que es presenten en aquest tesi. La secció de la tesi que tracta de l’anàlisi de partitures musicals se centra en l’anàlisi estructural a nivell de secció i de frase musical. Els límits temporals de les frases musicals s’identifiquen automàticament gràcies a un metodologia de segmentació d’última generació. Els límits de les seccions s’extreuen utilitzant un seguit de regles heurístiques determinades pel format de les partitures musicals. Posteriorment s’utilitza un nou mètode basat en anàlisi gràfic per establir semblances entre aquest elements estructurals en termes de melodia i text. També s’utilitza aquest mètode per etiquetar les relacions semiòtiques existents. La següent secció de la tesi tracta sobre anàlisi d’àudio i en particular revisa les tecnologies d’avantguardia d’anàlisi dels aspectes melòdics en OTMM. S’hi proposen adaptacions dels mètodes d’extracció de melodia existents que s’ajusten a OTMM. També s’hi presenten millores en metodologies de reconeixement de makam i en identificació de tònica basats en distribució de to. La metodologia d’alineament d’àudio amb partitura és el nucli de la tesi. Aquesta aborda els reptes culturalment específics imposats per les característiques musicals, les representacions de la teoria musical i la pràctica oral particulars de l’OTMM. Utilitzant diverses tècniques tal i com Dynamic Time Warping, Hough Transform o models de Markov de durada variable, la metodologia d’alineament esta dissenyada per enfrontar les diferències estructurals entre partitures musicals i enregistraments d’àudio. El mètode és robust inclús en presència d’expressions musicals no anotades en la partitura, desviacions de tempo ocorregudes en les actuacions musicals i diferències de tònica i afinació. La metodologia aprofita els resultats de l’anàlisi de la partitura i l’àudio per enllaçar la informació simbòlica amb l’àudio. A més, la tècnica d’alineament s’utilitza per obtenir descripcions de l’àudio fonamentades en la partitura. L’anàlisi de l’àudio fonamentat en la partitura no només simplifica les fases d’extracció de característiques d’àudio que requeririen de mètodes de processament d’àudio sofisticats, sinó que a més millora substancialment els resultats comparat amb altres mètodes d´ultima generació que només depenen de contingut d’àudio. Les metodologies d’anàlisi presentades s’han utilitzat per analitzar el corpus CompMusic Ottoman-Turkish makam music i s’han integrat en una aplicació web destinada al descobriment musical de tradicions culturals específiques. Algunes de les metodologies ja han sigut també aplicades a altres tradicions musicals com la Hindustani, la Carnàtica i la Grega. Seguint els preceptes de la investigació oberta totes les dades creades, eines computacionals i resultats dels anàlisis estan disponibles obertament. Tant les metodologies, les eines i el corpus en si mateix proporcionen àmplies oportunitats per recerques futures en diversos camps de recerca tal i com la musicologia computacional, la extracció d’informació musical i la educació musical. Traducció d’anglès a català per Oriol Romaní Picas.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada
NASA’s use of high bandwidth packetized Consultative Committee for Space Data Systems (CCSDS) telemetry in future missions presents a great challenge to ground data system developers. These missions, including the Earth Observing System (EOS), call for high data rate interfaces and small packet sizes. Because each packet requires a similar amount of protocol processing, high data rates and small packet sizes dramatically increase the real-time workload on ground packet processing systems. NASA’s Goddard Space Flight Center has been developing packet processing subsystems for more than twelve years. Implementations of these subsystems have ranged from mini-computers to single-card VLSI multiprocessor subsystems. The latter subsystem, known as the VLSI Packet Processor, was first deployed in 1991 for use in support of the Solar Anomalous & Magnetospheric Particle Explorer (SAMPEX) mission. An upgraded version of this VMEBus card, first deployed for Space Station flight hardware verification, has demonstrated sustained throughput of up to 50 Megabits per second and 15,000 packets per second. Future space missions including EOS will require significantly higher data and packet rate performance. A new approach to packet processing is under development that will not only increase performance levels by at least a factor of six but also reduce subsystem replication costs by a factor of five. This paper will discuss the development of a next generation packet processing subsystem and the architectural changes necessary to achieve a thirty-fold improvement in the performance/price of real-time packet processing.

International Telemetering Conference Proceedings / October 30-November 02, 1989 / Town & Country Hotel & Convention Center, San Diego, California
This paper describes a data acquisition, processing and display system which is suitable for various telemetry applications. The system can be connected either to a PCM encoder or to a telemetry decommutator through a built-in interface and can directly address any channel from the PCM stream for processing. Its compact size and simplicity allow it to be used in the flight line as a test console, in mobile stations as the main data processing system, or on-board test civil aircrafts for in-flight monitoring and data processing.

AbstractTo remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of event processing system architectures, with its main characteristics and components, followed by a survey of some of the most prominent commercial and open source tools. We then describe how we applied this technology in two of the DataBio project domains: agriculture and fishery. The devised generic pipeline for IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide real-time alerts for operational decision-making.

Deep learning has become the standard procedure to deal with Music Information Retrieval problems. This category of machine learning algorithms has achieved state-of-the-art results in several tasks, such as classification and auto-tagging. However, obtaining a good-performing model requires a significant amount of data. At the same time, most of the music datasets available lack cultural diversity. Therefore, the performance of the currently most used pre-trained models on underrepresented music genres is unknown. If music models follow the same direction that language models in Natural Language Processing, they should have poorer performance on music styles that are not present in the data used to train them. To verify this assumption, we use a well-known music model designed for auto-tagging in the task of genre recognition. We trained this model from scratch using a large general-domain dataset and two subsets specifying different domains. We empirically show that models trained on specific-domain data perform better than generalist models to classify music in the same domain, even trained with a smaller dataset. This outcome is distinctly observed in the subset that mainly contains Brazilian music, including several usually underrepresented genres.

Machine learning techniques have become a vital part of every ongoing research in technical areas. In recent times the world has witnessed many beautiful applications of machine learning in a practical sense which amaze us in every aspect. This paper is all about whether we should always rely on deep learning techniques or is it really possible to overcome the performance of simple deep learning algorithms by simple statistical machine learning algorithms by understanding the application and processing the data so that it can help in increasing the performance of the algorithm by a notable amount. The paper mentions the importance of data pre-processing than that of the selection of the algorithm. It discusses the functions involving trigonometric, logarithmic, and exponential terms and also talks about functions that are purely trigonometric. Finally, we discuss regression analysis on music signals.

Sonification and data processing algorithms have advanced over the years to reach practical applications in our everyday life. Similarly, image processing techniques have improved over time. While a number of image sonification methods have already been developed, few have delved into potential synergies through the combined use of multiple data and image processing techniques. Additionally, little has been done on the use of image sonification for artworks, as most research has been focused on the transcription of visual data for people with visual impairments. Our goal is to sonify paintings reflecting their art style and genre to improve the experience of both sighted and visually impaired individuals. To this end, we have designed initial sonifications for paintings of abstractionism and realism, and conducted interviews with visual and auditory experts to improve our mappings. We believe the recommendations and design directions we have received will help develop a multidimensional sonification algorithm that can better transcribe visual art into appropriate music.

In advertising and marketing, information is often repeatedly presented to consumers to increase their interest and sensitivity. This method is based on what is known in psychology as the ‘mere exposure effect’. In contrast, there is a growing interest in "neuromarketing," the application of brain science ideas to marketing, and the relationship between various psychological effects and brain activity in marketing. Brain measurements have also been used to study the mere exposure effect.However, although there have been various neuroscientific studies and verifications of the mere exposure effect, there have been few experiments that involve multiple exposure to stimuli across days. Therefore, we measured brain activity to investigate the effect of stimulus presentation across multiple days on impressions.In this study, we conducted an experiment in which subjects listened to music every day for a week. On the first day, when the subjects listened to the music for the first time, we conducted subjective evaluations of liking and brain function measurements. The next day, they listened to the music once every day, at home, for five days. On the last day, six days after the first day, we conducted the same subjective evaluation and brain function measurements as on the first day. To create music stimuli that the subjects had never perceived before, an automatic music creation tool was used.During brain activity measurement using optical topography, we focused on the change in the impression of likeability. In optical topography, the change in the concentration of Oxy-Hb in the brain blood flow was measured as a time-series data volume based on functional near-infrared spectroscopy (fNIRS). In the human brain, the amount of Oxy-Hb in specific activity areas increases with time. By measuring the increase and decrease in Oxy-Hb, we can understand how the subject responds to stimuli.We experimented with 10 healthy right-handed undergraduate and graduate students in their 20s (8 men and 2 women, average age 22.6 years) who provided informed consent, following the rules of the Ethics Committee of Chuo University.Consequently, the verification of impression evaluation, which is a subjective evaluation, showed that the impression evaluation increased significantly from the first day to the last day. At this point, it can be said that the mere exposure effect occurred through repeated listening. An analysis of the brain blood flow data showed that the prefrontal cortex became more active during the processing of negative impressions. In particular, the activity of the DLPFC may be deeply involved in the judgment of impressions. Although this was considered a hypothetical event in the previous study, it was clarified in this study without contradicting the data. The results also suggest a new possibility that the brain activity of first impressions can be used to estimate how impressions change in the future. These results may be useful in the field of neuromarketing for predicting long-term advertising effects.

The Canadian contribution and data set prepared as part of the Global Media and Internet Concentration (GMIC) project offers an independent academic, empirical and data-driven analysis of a deceptively simple yet profoundly important question: have telecom, media and internet markets become more concentrated over time, or less? Media Ownership and Concentration is presented from more than a dozen sectors of the telecom-media-internet industries, including film, music and book industries.

The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

Ground-penetrating radar (GPR) is a nondestructive geophysical technique used to create images of the subsurface. A major limitation of GPR is that a subject matter expert (SME) needs to post-process and interpret the data, limiting the technique’s use. Post-processing is time-intensive and, for detailed processing, requires proprietary software. The goal of this study is to develop automated GPR post-processing software, compatible with Geophysical Survey Systems, Inc. (GSSI) data, in open-source R programming. This would eliminate the need for an SME to process GPR data, remove proprietary software dependencies, and render GPR more accessible. This study collected GPR profiles by using a GSSI SIR4000 control unit, a 100 MHz antenna, and a Trimble GPS. A standardized method for post-processing data was then established, which includes static data removal, time-zero correction, distance normalization, data filtering, and stacking. These steps were scripted and automated in R programming, excluding data filtering, which was used from an existing package, RGPR. The study compared profiles processed using GSSI software to profiles processed using the R script developed here to ensure comparable functionality and output. While an SME is currently still necessary for interpretations, this script eliminates the need for one to post-process GSSI GPR data.