Relevant bibliographies by topics / Music Performance Classification Data processing

Academic literature on the topic 'Music Performance Classification Data processing'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Music Performance Classification Data processing"

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Sudarma, Made, and I. Gede Harsemadi. "Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 486. http://dx.doi.org/10.11591/ijece.v7i1.pp486-495.

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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.
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Manoharan, J. Samuel. "Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing." December 2021 3, no. 4 (December 24, 2021): 365–74. http://dx.doi.org/10.36548/jaicn.2021.4.008.

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Sound event detection, speech emotion classification, music classification, acoustic scene classification, audio tagging and several other audio pattern recognition applications are largely dependent on the growing machine learning technology. The audio pattern recognition issues are also addressed by neural networks in recent days. The existing systems operate within limited durations on specific datasets. Pretrained systems with large datasets in natural language processing and computer vision applications over the recent years perform well in several tasks. However, audio pattern recognition research with large-scale datasets is limited in the current scenario. In this paper, a large-scale audio dataset is used for training a pre-trained audio neural network. Several audio related tasks are performed by transferring this audio neural network. Several convolution neural networks are used for modeling the proposed audio neural network. The computational complexity and performance of this system are analyzed. The waveform and leg-mel spectrogram are used as input features in this architecture. During audio tagging, the proposed system outperforms the existing systems with a mean average of 0.45. The performance of the proposed model is demonstrated by applying the audio neural network to five specific audio pattern recognition tasks.
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Yang, Daniel, Kevin Ji, and TJ Tsai. "A Deeper Look at Sheet Music Composer Classification Using Self-Supervised Pretraining." Applied Sciences 11, no. 4 (February 4, 2021): 1387. http://dx.doi.org/10.3390/app11041387.

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This article studies a composer style classification task based on raw sheet music images. While previous works on composer recognition have relied exclusively on supervised learning, we explore the use of self-supervised pretraining methods that have been recently developed for natural language processing. We first convert sheet music images to sequences of musical words, train a language model on a large set of unlabeled musical “sentences”, initialize a classifier with the pretrained language model weights, and then finetune the classifier on a small set of labeled data. We conduct extensive experiments on International Music Score Library Project (IMSLP) piano data using a range of modern language model architectures. We show that pretraining substantially improves classification performance and that Transformer-based architectures perform best. We also introduce two data augmentation strategies and present evidence that the model learns generalizable and semantically meaningful information.
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Singhal, Rahul, Shruti Srivatsan, and Priyabrata Panda. "Classification of Music Genres using Feature Selection and Hyperparameter Tuning." September 2022 4, no. 3 (August 25, 2022): 167–78. http://dx.doi.org/10.36548/jaicn.2022.3.003.

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The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.
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Vani Vivekanand, Chettiyar. "Performance Analysis of Emotion Classification Using Multimodal Fusion Technique." Journal of Computational Science and Intelligent Technologies 2, no. 1 (April 16, 2021): 14–20. http://dx.doi.org/10.53409/mnaa/jcsit/2103.

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As the central processing unit of the human body, the human brain is in charge of several activities, including cognition, perception, emotion, attention, action, and memory. Emotions have a significant impact on human well-being in their life. Methodologies for accessing emotions of human could be essential for good user-machine interactions. Comprehending BCI (Brain-Computer Interface) strategies for identifying emotions can also help people connect with the world more naturally. Many approaches for identifying human emotions have been developed using signals of EEG for classifying happy, neutral, sad, and angry emotions, discovered to be effective. The emotions are elicited by various methods, including displaying participants visuals of happy and sad facial expressions, listening to emotionally linked music, visuals, and, sometimes, both of these. In this research, a multi-model fusion approach for emotion classification utilizing BCI and EEG data with various classifiers was proposed. The 10-20 electrode setup was used to gather the EEG data. The emotions were classified using the sentimental analysis technique based on user ratings. Simultaneously, Natural Language Processing (NLP) is implemented for increasing accuracy. This analysis classified the assessment parameters as happy, neutral, sad, and angry emotions. Based on these emotions, the proposed model’s performance was assessed in terms of accuracy and overall accuracy. The proposed model has a 93.33 percent overall accuracy and increased performance in all emotions identified.
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Grollmisch, Sascha, and Estefanía Cano. "Improving Semi-Supervised Learning for Audio Classification with FixMatch." Electronics 10, no. 15 (July 28, 2021): 1807. http://dx.doi.org/10.3390/electronics10151807.

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Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.
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Ma, Bo Le, Jing Fang Cheng, and Chao Ran Zhang. "Research on a New Array-Manifold of Single Vector Hydrophone." Advanced Materials Research 955-959 (June 2014): 899–910. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.899.

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For the purpose of improving the signal processing of single vector hydrophone, this paper combined two velocity signals as two complex data, so as to change array-manifold of single vector hydrophone. Taking two-dimension single vector hydrophone as an example, this paper compared the capability of signal processing of new array-manifold single vector hydrophone with old one from conventional beam-forming(CBF) ,minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC). As for CBF, the analysis indicates, the capability of spatial filtering of new array-manifold could improve 0.51db and the HPBW of new array-manifold will be smaller than old array-manifold. When the noise power is 0, the HPBW of new array-manifold will be narrower than old array-manifold 19.26°. As for MVDR, the capability of signal processing of new array-manifold is the same as old array-manifold. In MUSIC algorithm, the value measuring angle resolution shows the superiority of the new array-manifold- angle resolution. Simulation and measured data proved the better performance of the method presented by this paper.
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Dwisaputra, Indra, and Ocsirendi Ocsirendi. "Teknik Pengenalan Suara Musik Pada Robot Seni Tari." Manutech : Jurnal Teknologi Manufaktur 10, no. 02 (May 20, 2019): 35–39. http://dx.doi.org/10.33504/manutech.v10i02.66.

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The dancing robot has become an annual competition in Indonesia that needs to be developed to improve robot performance. The dancing robot is a humanoid robot that has 24 degrees of freedom. For 2018 the theme raised was "Remo Dancer Robot". Sound processing provides a very important role in dance robots. This robot moves dancing to adjust to the rhythm of the music. The robot will stop dancing when the music is mute. The resulting sound signal is still analogous. Voice signals must be changed to digital data to access the signal. Convert analog to digital signals using Analog Digital Converter (ADC). ADC data is taken by sampling time 254 data per second. The sampling data is stored and grouped per 1 second to classify the parts of Remo Dance music. The results of data classification are in the form of digital numbers which then become a reference to determine the movement of the robot. Robots can recognize conditions when music is in a mute or a play condition.
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Wang, Guoxuan, Guimei Zheng, Hongzhen Wang, and Chen Chen. "Meter Wave Polarization-Sensitive Array Radar for Height Measurement Based on MUSIC Algorithm." Sensors 22, no. 19 (September 26, 2022): 7298. http://dx.doi.org/10.3390/s22197298.

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Obtaining good measurement performance with meter wave radar has always been a difficult problem. Especially in low-elevation areas, the multipath effect seriously affects the measurement accuracy of meter wave radar. The generalized multiple signal classification (MUSIC) algorithm is a well-known measurement method that dose not require decorrelation processing. The polarization-sensitive array (PSA) has the advantage of polarization diversity, and the polarization smoothing generalized MUSIC algorithm demonstrates good angle estimation performance in low-elevation areas when based on a PSA. Nevertheless, its computational complexity is still high, and the estimation accuracy and discrimination success probability need to be further improved. In addition, it cannot estimate the polarization parameters. To solve these problems, a polarization synthesis steering vector MUSIC algorithm is proposed in this paper. First, the MUSIC algorithm is used to obtain the spatial spectrum of the meter wave PSA. Second, the received data are properly deformed and classified. The Rayleigh–Ritz method is used to decompose the angle to realize the decoupling of polarization and the direction of the arrival angle. Third, the geometric relationship and prior information of the direct wave and the reflected wave are used to continue dimension reduction processing to reduce the computational complexity of the algorithm. Finally, the geometric relationship is used to obtain the target height measurement results. Extensive simulation results illustrate the accuracy and superiority of the proposed algorithm.
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Liang, Yan, Zhou Meng, Yu Chen, Yichi Zhang, Mingyang Wang, and Xin Zhou. "A Data Fusion Orientation Algorithm Based on the Weighted Histogram Statistics for Vector Hydrophone Vertical Array." Sensors 20, no. 19 (October 1, 2020): 5619. http://dx.doi.org/10.3390/s20195619.

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In this paper, we propose a data fusion algorithm based on the weighted histogram statistics (DF-WHS) to improve the performance of direction-of-arrival (DOA) estimation for the vector hydrophone vertical array (VHVA). The processing frequency band is firstly divided into multiple sub-bands, and the high-resolution multiple signal classification (MUSIC) algorithm is applied to estimate the azimuth of each sub-band for each vector hydrophone. Then, the weighted least square (WLS) data fusion technique is used to fuse the sub-band estimation results of multiple sensors. Finally, the weighted histogram statistics method is employed to obtain the synthesis results in the frequency domain. We carried out a simulation and sea trial of the 16-element VHVA to evaluate the performance of the proposed algorithm. Compared to several traditional processing algorithms, the beam width of the proposed approach is significantly narrower, the side lobes are considerably lower, and the mean square error (MSE) is effectively smaller. In addition, the DF-WHS method is more suitable to accurately estimate the target azimuth with a low signal-to-noise ratio (SNR) because the noise sub-band is suppressed in the weighted histogram statistics step. The DF-WHS method in this article provides a new approach to improve the performance of deep-sea target detection for the VHVA.
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Dissertations / Theses on the topic "Music Performance Classification Data processing"

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McKay, Cory. "Automatic genre classification of MIDI recordings." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=81503.

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A software system that automatically classifies MIDI files into hierarchically organized taxonomies of musical genres is presented. This extensible software includes an easy to use and flexible GUI. An extensive library of high-level musical features is compiled, including many original features. A novel hybrid classification system is used that makes use of hierarchical, flat and round robin classification. Both k-nearest neighbour and neural network-based classifiers are used, and feature selection and weighting are performed using genetic algorithms. A thorough review of previous research in automatic genre classification is presented, along with an overview of automatic feature selection and classification techniques. Also included is a discussion of the theoretical issues relating to musical genre, including but not limited to what mechanisms humans use to classify music by genre and how realistic genre taxonomies can be constructed.
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Fiebrink, Rebecca. "An exploration of feature selection as a tool for optimizing musical genre classification /." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99372.

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The computer classification of musical audio can form the basis for systems that allow new ways of interacting with digital music collections. Existing music classification systems suffer, however, from inaccuracy as well as poor scalability. Feature selection is a machine-learning tool that can potentially improve both accuracy and scalability of classification. Unfortunately, there is no consensus on which feature selection algorithms are most appropriate or on how to evaluate the effectiveness of feature selection. Based on relevant literature in music information retrieval (MIR) and machine learning and on empirical testing, the thesis specifies an appropriate evaluation method for feature selection, employs this method to compare existing feature selection algorithms, and evaluates an appropriate feature selection algorithm on the problem of musical genre classification. The outcomes include an increased understanding of the potential for feature selection to benefit MIR and a new technique for optimizing one type of classification-based system.
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Phillips, Rhonda D. "A Probabilistic Classification Algorithm With Soft Classification Output." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/26701.

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This thesis presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection), a novel data reduction technique that can be used in conjunction with PIGSCR (parallel IGSCR), a noise removal method based on the maximum noise fraction (MNF), and a continuous version of IGSCR (CIGSCR) that outputs soft classifications. All of the above are either classification algorithms or preprocessing algorithms necessary prior to the classification of high dimensional, noisy images. PIGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The feature reduction method introduced in this thesis is based on the singular value decomposition (SVD). This feature reduction technique demonstrated that SVD-based feature reduction can lead to more accurate IGSCR classifications than PCA-based feature reduction. This thesis describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. The adaptive filtering scheme improves image quality as shown by estimated SNRs and classification accuracy improvements greater than 10%. The continuous iterative guided spectral class rejection (CIGSCR) classification method is based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. This type of semisupervised classification method is particularly useful in remote sensing where datasets are large, training data are difficult to acquire, and clustering makes the identification of subclasses adequate for training purposes less difficult. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that influence iterations.
Ph. D.
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Sanden, Christopher, and University of Lethbridge Faculty of Arts and Science. "An empirical evaluation of computational and perceptual multi-label genre classification on music / Christopher Sanden." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2602.

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Automatic music genre classi cation is a high-level task in the eld of Music Information Retrieval (MIR). It refers to the process of automatically assigning genre labels to music for various tasks, including, but not limited to categorization, organization and browsing. This is a topic which has seen an increase in interest recently as one of the cornerstones of MIR. However, due to the subjective and ambiguous nature of music, traditional single-label classi cation is inadequate. In this thesis, we study multi-label music genre classi cation from perceptual and computational perspectives. First, we design a set of perceptual experiments to investigate the genre-labelling behavior of individuals. The results from these experiments lead us to speculate that multi-label classi cation is more appropriate for classifying music genres. Second, we design a set of computational experiments to evaluate multi-label classi cation algorithms on music. These experiments not only support our speculation but also reveal which algorithms are more suitable for music genre classi cation. Finally, we propose and examine a group of ensemble approaches for combining multi-label classi cation algorithms to further improve classi cation performance. ii
viii, 87 leaves ; 29 cm
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Klinkradt, Bradley Hugh. "An investigation into the application of the IEEE 1394 high performance serial bus to sound installation contro." Thesis, Rhodes University, 2003. http://hdl.handle.net/10962/d1004899.

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This thesis investigates the feasibility of using existing IP-based control and monitoring protocols within professional audio installations utilising IEEE 1394 technology. Current control and monitoring technologies are examined, and the characteristics common to all are extracted and compiled into an object model. This model forms the foundation for a set of evaluation criteria against which current and future control and monitoring protocols may be measured. Protocols considered include AV/C, MIDI, QSC-24, and those utilised within the UPnP architecture. As QSC-24 and the UPnP architecture are IP-based, the facilities required to transport IP datagrams over the IEEE 1394 bus are investigated and implemented. Example QSC-24 and UPnP architecture implementations are described, which permit the control and monitoring of audio devices over the IEEE 1394 network using these IP-based technologies. The way forward for the control and monitoring of professional audio devices within installations is considered, and recommendations are provided.
KMBT_363
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Jürgensen, Frauke. "Accidentals in the mid-fifteenth century : a computer-aided study of the Buxheim organ book and its concordances." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85921.

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The Buxheim Organ Book, the largest fifteenth-century manuscript of keyboard tablature, has never before been examined as a whole in light of musica ficta issues, although it contains far more accidentals than any contemporaneous source in mensural notation. Although tablature has been used by various scholars to examine accidentals in sixteenth-century music, studies of fifteenth-century accidentals have focussed on theoretical evidence and small groups of pieces from mensural sources. The author uses the Buxheim Organ Book to extend the investigations of accidentals in tablature back into the fifteenth century, combining the large data set provided by this manuscript with a statistical approach modelled on that of Thomas Brothers's smaller-scale study of the chansons of Binchois. Specialised computer programs are introduced, which detect musical structures relevant to the analysis of Renaissance music such as different types of cadential voice leading. These programs function as extensions to David Huron's Humdrum Toolkit. With these tools, signing practises in the intabulations are statistically compared with all of the concordances of the models. Conclusions are suggested pertaining to issues of signature accidental transmission, partial signatures, mode, and musica ficta, which can be used as a contextual backdrop for the analysis of individual pieces. The evidence provided by the accidentals in Buxheim and its concordances draws a clear picture of how a group of fifteenth-century musicians added accidentals to polyphonic music. For the first time, this study provides us with principles and guidelines for musica ficta -decisions based on actual practice.
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Laurier, Cyril François. "Automatic Classification of musical mood by content-based analysis." Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/51582.

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In this work, we focus on automatically classifying music by mood. For this purpose, we propose computational models using information extracted from the audio signal. The foundations of such algorithms are based on techniques from signal processing, machine learning and information retrieval. First, by studying the tagging behavior of a music social network, we find a model to represent mood. Then, we propose a method for automatic music mood classification. We analyze the contributions of audio descriptors and how their values are related to the observed mood. We also propose a multimodal version using lyrics, contributing to the field of text retrieval. Moreover, after showing the relation between mood and genre, we present a new approach using automatic music genre classification. We demonstrate that genre-based mood classifiers give higher accuracies than standard audio models. Finally, we propose a rule extraction technique to explicit our models.
En esta tesis, nos centramos en la clasificación automática de música a partir de la detección de la emoción que comunica. Primero, estudiamos cómo los miembros de una red social utilizan etiquetas y palabras clave para describir la música y las emociones que evoca, y encontramos un modelo para representar los estados de ánimo. Luego, proponemos un método de clasificación automática de emociones. Analizamos las contribuciones de descriptores de audio y cómo sus valores están relacionados con los estados de ánimo. Proponemos también una versión multimodal de nuestro algoritmo, usando las letras de canciones. Finalmente, después de estudiar la relación entre el estado de ánimo y el género musical, presentamos un método usando la clasificación automática por género. A modo de recapitulación conceptual y algorítmica, proponemos una técnica de extracción de reglas para entender como los algoritmos de aprendizaje automático predicen la emoción evocada por la música
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Kästel, Arne Morten, and Christian Vestergaard. "Comparing performance of K-Means and DBSCAN on customer support queries." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260252.

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In customer support, there are often a lot of repeat questions, and questions that does not need novel answers. In a quest to increase the productivity in the question answering task within any business, there is an apparent room for automatic answering to take on some of the workload of customer support functions. We look at clustering corpora of older queries and texts as a method for identifying groups of semantically similar questions and texts that would allow a system to identify new queries that fit a specific cluster to receive a connected, automatic response. The approach compares the performance of K-means and density-based clustering algorithms on three different corpora using document embeddings encoded with BERT. We also discuss the digital transformation process, why companies are unsuccessful in their implementation as well as the possible room for a new more iterative model.
I kundtjänst förekommer det ofta upprepningar av frågor samt sådana frågor som inte kräver unika svar. I syfte att öka produktiviteten i kundtjänst funktionens arbete att besvara dessa frågor undersöks metoder för att automatisera en del av arbetet. Vi undersöker olika metoder för klusteranalys, applicerat på existerande korpusar innehållande texter så väl som frågor. Klusteranalysen genomförs i syfte att identifiera dokument som är semantiskt lika, vilket i ett automatiskt system för frågebevarelse skulle kunna användas för att besvara en ny fråga med ett existerande svar. En jämförelse mellan hur K-means och densitetsbaserad metod presterar på tre olika korpusar vars dokumentrepresentationer genererats med BERT genomförs. Vidare diskuteras den digitala transformationsprocessen, varför företag misslyckas avseende implementation samt även möjligheterna för en ny mer iterativ modell.
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Shafer, Seth. "Recent Approaches to Real-Time Notation." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984210/.

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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.
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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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Books on the topic "Music Performance Classification Data processing"

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1946-, Beall Julianne, ed. DDC, Dewey decimal classification: 004-006 data processing and computer science and changes in related disciplines. Albany, NY: Forest Press, Division of the Lake Placid Education Foundation, 1985.

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Dewey, Melvil. DDC, Dewey decimal classification.: Revision of edition 19. Albany, N.Y., U.S.A: Forest Press, 1985.

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The sound on sound book of live sound for the performing musician. London: Sanctuary Publishing, 1998.

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Office, General Accounting. District of Columbia: Comments on fiscal year 2000 performance report : report to congressional subcommittees. Washington, D.C: U.S. General Accounting Office, 2001.

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Office, General Accounting. District of Columbia: Performance report reflects progress and opportunities for improvement : report to congressional subcommittees. [Washington, D.C.]: The Office, 2002.

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Gabrielli, Leonardo, and Stefano Squartini. Wireless Networked Music Performance. Springer Singapore Pte. Limited, 2016.

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Gabrielli, Leonardo, and Stefano Squartini. Wireless Networked Music Performance. Springer London, Limited, 2016.

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Guide To Computing For Expressive Music Performance. Springer, 2012.

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Mak, Kitman, and Chris Frost. A. I. Performance : the Art of Live Automation: The Ultimate 'how to' Guide in Creating Stunning, Technical and Revolutionary Live Shows for Any Contemporary Musical Performer. Independently Published, 2018.

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Mak, Kitman, and Chris Frost. A. I. Performance : The Art of Live Automation: The Ultimate 'how to' Guide in Creating Stunning, Technical and Revolutionary Live Shows for Any Contemporary Musical Performer. Independently Published, 2018.

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Book chapters on the topic "Music Performance Classification Data processing"

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Vatolkin, Igor, Wolfgang Theimer, and Martin Botteck. "Partition Based Feature Processing for Improved Music Classification." In Challenges at the Interface of Data Analysis, Computer Science, and Optimization, 411–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24466-7_42.

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Kralj Novak, Petra, Teresa Scantamburlo, Andraž Pelicon, Matteo Cinelli, Igor Mozetič, and Fabiana Zollo. "Handling Disagreement in Hate Speech Modelling." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 681–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08974-9_54.

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AbstractHate speech annotation for training machine learning models is an inherently ambiguous and subjective task. In this paper, we adopt a perspectivist approach to data annotation, model training and evaluation for hate speech classification. We first focus on the annotation process and argue that it drastically influences the final data quality. We then present three large hate speech datasets that incorporate annotator disagreement and use them to train and evaluate machine learning models. As the main point, we propose to evaluate machine learning models through the lens of disagreement by applying proper performance measures to evaluate both annotators’ agreement and models’ quality. We further argue that annotator agreement poses intrinsic limits to the performance achievable by models. When comparing models and annotators, we observed that they achieve consistent levels of agreement across datasets. We reflect upon our results and propose some methodological and ethical considerations that can stimulate the ongoing discussion on hate speech modelling and classification with disagreement.
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"Classification performance of data mining algorithms applied to breast cancer data." In Computational Vision and Medical Image Processing IV, 325–30. CRC Press, 2013. http://dx.doi.org/10.1201/b15810-58.

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Zhu, Qiusha, Lin Lin, Mei-Ling Shyu, and Dianting Liu. "Utilizing Context Information to Enhance Content-Based Image Classification." In Multimedia Data Engineering Applications and Processing, 114–30. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2940-0.ch006.

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Traditional image classification relies on text information such as tags, which requires a lot of human effort to annotate them. Therefore, recent work focuses more on training the classifiers directly on visual features extracted from image content. The performance of content-based classification is improving steadily, but it is still far below users’ expectation. Moreover, in a web environment, HTML surrounding texts associated with images naturally serve as context information and are complementary to content information. This paper proposes a novel two-stage image classification framework that aims to improve the performance of content-based image classification by utilizing context information of web-based images. A new TF*IDF weighting scheme is proposed to extract discriminant textual features from HTML surrounding texts. Both content-based and context-based classifiers are built by applying multiple correspondence analysis (MCA). Experiments on web-based images from Microsoft Research Asia (MSRA-MM) dataset show that the proposed framework achieves promising results.
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Meng, Tao, Mei-Ling Shyu, and Lin Lin. "Multimodal Information Integration and Fusion for Histology Image Classification." In Multimedia Data Engineering Applications and Processing, 35–50. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2940-0.ch003.

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Biomedical imaging technology has become an important tool for medical research and clinical practice. A large amount of imaging data is generated and collected every day. Managing and analyzing these data sets require the corresponding development of the computer based algorithms for automatic processing. Histology image classification is one of the important tasks in the bio-image informatics field and has broad applications in phenotype description and disease diagnosis. This study proposes a novel framework of histology image classification. The original images are first divided into several blocks and a set of visual features is extracted for each block. An array of C-RSPM (Collateral Representative Subspace Projection Modeling) models is then built that each model is based on one block from the same location in original images. Finally, the C-Value Enhanced Majority Voting (CEWMV) algorithm is developed to derive the final classification label for each testing image. To evaluate this framework, the authors compare its performance with several well-known classifiers using the benchmark data available from IICBU data repository. The results demonstrate that this framework achieves promising performance and performs significantly better than other classifiers in the comparison.
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Tarle, Balasaheb, and M. Akkalakshmi. "Integrating Multiple Techniques to Enhance Medical Data Classification." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 252–74. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9121-5.ch012.

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Improving classification performance is an essential task in medical data classification. In the current medical data classification technique, if data pre-processing is not performed, the approach is more time consuming and has less classification accuracy. Here, the authors proposed two pre-processing techniques for enhancing the classification performance on medical data. The first pre-processing technique is noise filtering to improve the data quality. The second pre-processing bag of words technique is used for better feature selection. Subsequently, the hybrid fuzzy neural network approach is used for classification to handle data imprecision during classification. This arrangement of data pre-processing and the fuzzy neural classifier method improve classification accuracy.
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Joshi, Deepak, and Michael E. Hahn. "Electromyogram and Inertial Sensor Signal Processing in Locomotion and Transition Classification." In Data Analytics in Medicine, 762–78. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1204-3.ch041.

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Signal processing in biomedical engineering is essentially required for classification while serving mainly two aims. The first is noise removal and the second is signal representation. Signal representation deals with transforming the signal in such a way that the signal is most informative in that particular domain for the application at hand. This chapter will describe signal processing methods like spectrogram with specific applications to locomotion and transition classification using Electromyography (EMG) data. A wavelet analysis application on foot acceleration signals for automatic identification of toe off in locomotion and the ramp transition is also shown. Finally, the performance of EMG and accelerometer performance across different time windows of a gait cycle in locomotion and transition classification is presented with an emphasis on fusing the data from both sensors for better classification.
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Weese, Josh. "Predictive Analytics in Digital Signal Processing." In Advances in Data Mining and Database Management, 223–53. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5063-3.ch010.

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Pitch detection and instrument identification can be achieved with relatively high accuracy when considering monophonic signals in music; however, accurately classifying polyphonic signals in music remains an unsolved research problem. Pitch and instrument classification is a subset of Music Information Retrieval (MIR) and automatic music transcription, both having numerous research and real-world applications. Several areas of research are covered in this chapter, including the fast Fourier transform, onset detection, convolution, and filtering. Polyphonic signals with many different voices and frequencies can be exceptionally complex. This chapter presents a new model for representing the spectral structure of polyphonic signals: Uniform MAx Gaussian Envelope (UMAGE). The new spectral envelope precisely approximates the distribution of frequency parts in the spectrum while still being resilient to oscillating rapidly and is able to generalize well without losing the representation of the original spectrum.
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Singh, Deepak, Dilip Singh Sisodia, and Pradeep Singh. "Genetic Algorithm Based Pre-Processing Strategy for High Dimensional Micro-Array Gene Classification." In Nature-Inspired Algorithms for Big Data Frameworks, 22–46. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5852-1.ch002.

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Discretization is one of the popular pre-processing techniques that helps a learner overcome the difficulty in handling the wide range of continuous-valued attributes. The objective of this chapter is to explore the possibilities of performance improvement in large dimensional biomedical data with the alliance of machine learning and evolutionary algorithms to design effective healthcare systems. To accomplish the goal, the model targets the preprocessing phase and developed framework based on a Fisher Markov feature selection and evolutionary based binary discretization (EBD) for a microarray gene expression classification. Several experiments were conducted on publicly available microarray gene expression datasets, including colon tumors, and lung and prostate cancer. The performance is evaluated for accuracy and standard deviations, and is also compared with the other state-of-the-art techniques. The experimental results show that the EBD algorithm performs better when compared to other contemporary discretization techniques.
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Baharadwaj, Nitin, Sheena Wadhwa, Pragya Goel, Isha Sethi, Chanpreet Singh Arora, Aviral Goel, Sonika Bhatnagar, and Harish Parthasarathy. "De-Noising, Clustering, Classification, and Representation of Microarray Data for Disease Diagnostics." In Research Developments in Computer Vision and Image Processing, 149–74. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4558-5.ch009.

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A microarray works by exploiting the ability of a given mRNA molecule to bind specifically to the DNA template from which it originated under specific high stringency conditions. After this, the amount of mRNA bound to each DNA site on the array is determined, which represents the expression level of each gene. Qualification of the mRNA (probe) bound to each DNA spot (target) can help us to determine which genes are active or responsible for the current state of the cell. The probe target hybridization is usually detected and quantified using dyes/flurophore/chemiluminescence labels. The microarray data gives a single snapshot of the gene activity profile of a cell at any given time. Microarray data helps to elucidate the various genes involved in the disease and may also be used for diagnosis /prognosis. In spite of its huge potential, microarray data interpretation and use is limited by its error prone nature, the sheer size of the data and the subjectivity of the analysis. Initially, we describe the use of several techniques to develop a pre-processing methodology for denoising microarray data using signal process techniques. The noise free data thus obtained is more suitable for classification of the data as well as for mining useful information from the data. Discrete Fourier Transform (DFT) and Autocorrelation were explored for denoising the data. We also used microarray data to develop the use of microarray data as diagnostic tool in cancer using One Dimensional Fourier Transform followed by simple Euclidean Distance Calculations and Two Dimensional MUltiple SIgnal Classification (MUSIC). To improve the accuracy of the diagnostic tool, Volterra series were used to model the nonlinear behavior of the data. Thus, our efforts at denoising, representation, and classification of microarray data with signal processing techniques show that appreciable results could be attained even with the most basic techniques. To develop a method to search for a gene signature, we used a combination of PCA and density based clustering for inferring the gene signature of Parkinson’s disease. Using this technique in conjunction with gene ontology data, it was possible to obtain a signature comprising of 21 genes, which were then validated by their involvement in known Parkinson’s disease pathways. The methodology described can be further developed to yield future biomarkers for early Parkinson’s disease diagnosis, as well as for drug development.
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Conference papers on the topic "Music Performance Classification Data processing"

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Silva, Diego Furtado, Angelo Cesar Mendes da Silva, Luís Felipe Ortolan, and Ricardo Marcondes Marcacini. "On Generalist and Domain-Specific Music Classification Models and Their Impacts on Brazilian Music Genre Recognition." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbcm.2021.19427.

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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.
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Panchwagh, Mangesh M., and Vijay D. Katkar. "Music genre classification using data mining algorithm." In 2016 Conference on Advances in Signal Processing (CASP). IEEE, 2016. http://dx.doi.org/10.1109/casp.2016.7746136.

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Er, Mehmet Bilal, Harun CIg, and Umut Kuran. "Classification of Makam structures in Turkish art music with using artificial neural network." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090276.

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McKay, Cory, and Ichiro Fujinaga. "Improving automatic music classification performance by extracting features from different types of data." In the international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1743384.1743430.

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Suryaprakash, Raj Tejas, and Raj Rao Nadakuditi. "The performance of music-based DOA in white noise with missing data." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319826.

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Shimamura, Tetsuya, and Takeshi Yokose. "AR-model-based data extension to improve the Performance of MUSIC." In 2013 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2013. http://dx.doi.org/10.1109/ispacs.2013.6704593.

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Pavlopoulou, Christina, and Stella X. Yu. "Classification and feature selection with human performance data." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5650308.

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Powell, Harry C., John Lach, Maite Brandt-Pearce, and Charles L. Brown. "Systematic estimation of ANN classification performance employing synthetic data." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5589207.

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Das, Madhusmita, and Rasmita Dash. "Performance Analysis of Classification Techniques for Car Data Set Analysis." In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182332.

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Erkaymaz, Okan, and Tugba Palabas. "Classification of cervical cancer data and the effect of random subspace algorithms on classification performance." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404197.

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