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Recognition of music style from orchestral recording using Music Information Retrieval techniques
Jelínková, Jana ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
As all genres of popular music, classical music consists of many different subgenres. The aim of this work is to recognize those subgenres from orchestral recordings. It is focused on the time period from the very end of 16th century to the beginning of 20th century, which means that Baroque era, Classical era and Romantic era are researched. The Music Information Retrieval (MIR) method was used to classify chosen subgenres. In the first phase of MIR method, parameters were extracted from musical recordings and were evaluated. Only the best parameters were used as input data for machine learning classifiers, to be specific: kNN (K-Nearest Neighbor), LDA (Linear Discriminant Analysis), GMM (Gaussian Mixture Models) and SVM (Support Vector Machines). In the final chapter, all the best results are summarized. According to the results, there is significant difference between the Baroque era and the other researched eras. This significant difference led to better identification of the Baroque era recordings. On the contrary, Classical era ended up to be relatively similar to Romantic era and therefore all classifiers had less success in identification of recordings from this era. The results are in line with music theory and characteristics of chosen musical eras.
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Automatic tagging of musical compositions using machine learning methods
Semela, René ; Galáž, Zoltán (referee) ; Kiska, Tomáš (advisor)
One of the many challenges of machine learning are systems for automatic tagging of music, the complexity of this issue in particular. These systems can be practically used in the content analysis of music or the sorting of music libraries. This thesis deals with the design, training, testing, and evaluation of artificial neural network architectures for automatic tagging of music. In the beginning, attention is paid to the setting of the theoretical foundation of this field. In the practical part of this thesis, 8 architectures of neural networks are designed (4 fully convolutional and 4 convolutional recurrent). These architectures are then trained using the MagnaTagATune Dataset and mel spectrogram. After training, these architectures are tested and evaluated. The best results are achieved by the four-layer convolutional recurrent neural network (CRNN4) with the ROC-AUC = 0.9046 ± 0.0016. As the next step of the practical part of this thesis, a completely new Last.fm Dataset 2020 is created. This dataset uses Last.fm and Spotify API for data acquisition and contains 100 tags and 122877 tracks. The most successful architectures are then trained, tested, and evaluated on this new dataset. The best results on this dataset are achieved by the six-layer fully convolutional neural network (FCNN6) with the ROC-AUC = 0.8590 ± 0.0011. Finally, a simple application is introduced as a concluding point of this thesis. This application is designed for testing individual neural network architectures on a user-inserted audio file. Overall results of this thesis are similar to other papers on the same topic, but this thesis brings several new findings and innovations. In terms of innovations, a significant reduction in the complexity of individual neural network architectures is achieved while maintaining similar results.
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Design of Net-Based Virtual Collaborative Musical Instrument
Liudkevich, Denis ; Kiska, Tomáš (referee) ; Kavan, Jan (advisor)
The aim of this work was to create an online platform for multi-user sound creation with original sound synthesis tools. The educational context of the application was also taken into account by hiding the controls of the sound parameters behind the subconsciously known physical phenomena and the game form of the application. A substantial part of the logic and all graphics of the instruments is written in the JavaScript programming language and its library p5.js. It is located on the client side and communicates with the Node.js-based server via a web socket. The audio part is on another server in the SuperCollider environment, it is transmitted via IceCast and communicates with the main OSC message server. The application contains 3 instruments for generating sounds and one effects module. Each instrument is designed for multiple users and requires their cooperation. Acceptable transmission speeds and minimum computational demands have been achieved by optimizing the instrument's internal algorithms, the way in which the graphic content is displayed and the appropriate routing of the individual sound modules. The sound is specific for each instrument. The instruments in the application are tuned and designed so that the user can both achieve interesting sound results himself and play his role as a whole with others. Methods such as granular synthesis, chaotic oscillators, string instrument modeling, filter combinations, and so on are used to generate sound. Great emphasis in the development of the application was placed on the separation of roles, simultaneous control of one instrument by several players and communication of users through playing the instruments and text expression - chat. An important part is also a block for displaying descriptive information.
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Deep learning based sound event recognition
Bajzík, Jakub ; Kiska, Tomáš (referee) ; Přinosil, Jiří (advisor)
This paper deals with processing and recognition of events in audio signal. The work explores the possibility of using audio signal visualization and subsequent use of convolutional neural networks as a classifier for recognition in real use. Recognized audio events are gunshots placed in a sound background such as street noise, human voice, animal sounds, and other forms of random noise. Before the implementation, a large database with various parameters, especially reverberation and time positioning within the processed section, is created. In this work are used freely available platforms Keras and TensorFlow for work with neural networks.
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Music genre recognition using Music information retrieval techniques
Zemánková, Šárka ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
This diploma work deals with music genre recognition using the techniques of Music Information Retrieval. It contains a brief description of the principle of this research area and its subfield called Music Genre Recognition. The following chapter includes selection of the most suitable parameters for describing music genres. This work further characterizes machine learning methods used in this field of research. The next chapter deals with the descriptions of music datasets created for genre classification studies. Subsequently, there is a draft and evaluation of the system for music genre recognition. The last part of this work describes the results of partial parameter analysis, dependence of genre classification accuracy on the amount of parameters and contains a discussion on the causes of classification accurancy for the individual genres.
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Music information retrieval techniques for determining the place of origin of the Czech chamber and orchestral music interpretations
Miklánek, Štěpán ; Mekyska, Jiří (referee) ; Kiska, Tomáš (advisor)
This diploma thesis is focused on the statistical analysis of chamber and orchestral classical music recordings composed by Czech authors. One of the chapters is dedicated to the description of a feature extraction process that precedes the statistical analysis. Techniques of Music Information Retrieval are used during several stages of this thesis. Databases used for analysis are described and pre-processing steps are proposed. A tool for synchronization of the recordings was implemented in MATLAB. Finally the system used for classification of recordings based on their geographical origin is proposed. The recordings are sorted by a binary classifier into two categories of Czech and world recordings. The first part of the statistical analysis is focused on individual analysis of features. The features are evaluated based on their discrimination strength. The second part of the statistical analysis is focused on feature selection, which can improve the overall accuracy of the binary classifier compared to the individual analysis of the features.
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Musical instruments recognition from audio records using Music information retrieval techniques
Kárník, Radoslav ; Mucha, Ján (referee) ; Kiska, Tomáš (advisor)
This paper discusses design and implementation of classifying system for recognition of musical instruments from audio records with use of Musical Information Retrieval techniques. In the first part, paper describes parameters used for instrument classification, calculation of said parameters from records and reduction of feature vector. Next part is devoted to tuning and implementation of various classifiers with focus on neural networks. These classifiers ar further tested on records from IRMAS dataset wchich contain 11 musical instruments playing solo or with other instruments. Results of classifiers tested on different parameters and different numbers of instruments are discussed in the last part.
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Music mood and emotion recognition using Music information retrieval techniques
Smělý, Pavel ; Mucha, Ján (referee) ; Kiska, Tomáš (advisor)
This work focuses on scientific area called Music Information Retrieval, more precisely it’s subdivision focusing on the recognition of emotions in music called Music Emotion Recognition. The beginning of the work deals with general overview and definition of MER, categorization of individual methods and offers a comprehensive view of this discipline. The thesis also concentrates on the selection and description of suitable parameters for the recognition of emotions, using tools openSMILE and MIRtoolbox. A freely available DEAM database was used to obtain the set of music recordings and their subjective emotional annotations. The practical part deals with the design of a static dimensional regression evaluation system for numerical prediction of musical emotions in music recordings, more precisely their position in the AV emotional space. The thesis publishes and comments on the results obtained by individual analysis of the significance of individual parameters and for the overall analysis of the prediction of the proposed model.
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Modeling Sound Field in Closed Space at Low Frequencies
Hořák, Pavel ; Kiska, Tomáš (referee) ; Schimmel, Jiří (advisor)
This diploma thesis deals with issues of low frequency acoustics and simulation. In this work the FTDT method of simulation is used. Measuring and simulation are focused on live-sound system and are evaluated using basic sound system optimisation techniques. Main output of this work is verification of basic low-frequency acoustics principles using simulation and measuring.
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