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Making up missing audio signal sections
Pospíšil, Jiří ; Rášo, Ondřej (referee) ; Mach, Václav (advisor)
The goal of this bachelor’s thesis is to get introduced with methods for reconstruction of missing samples in audio signal using periodicity-based interpolation and AR model based interpolation. Further it’s introducing us with Audio Inpainting method based on sparse representation. In practical part there are programmed three algorithms based on these interpolation methods and described an algorithm which is used in Audio Inpainting. These algorithms are compared with objective methods, SNR measurements depending on gap length and value of input parameter.
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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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Restoration of damaged audio signals using autoregressive models
Soboňa, Matúš ; Rajmic, Pavel (referee) ; Mokrý, Ondřej (advisor)
The bachelor thesis deals with the problem of restoring audio signals damaged by sample loss, using autoregressive models. The restoration itself is solved by W. Etter and A. Janssen's algorithms. These algorithms are implemented in MATLAB and tested on artificial signals aswell as on real recordings. Algorithms are then compared based on quality of restoration dependent on different parameters of signals.
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Restoration of audio signals damaged by quantization
Šiška, Jakub ; Rajmic, Pavel (referee) ; Záviška, Pavel (advisor)
This master’s thesis deals with the restoration of audio signals damaged by quantization. The theoretical part starts with a description of quantization and dequantization in general, few existing methods of dequantization of audio signals and theory of sparse representations of signals are also presented. The next part introduces algorithms suitable for dequantization, specifically Douglas–Rachford, Chambolle–Pock, SPADEQ and implementation of these algorithms in MATLAB application in the next chapter. In the last part of this thesis, testing of reconstructed signals using the algorithms takes place and results are evaluated by objective measures SDR, PEMO-Q, PEAQ and subjective listening test MUSHRA.
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Research of dynamics features comparing audio records
Zemánková, Šárka ; Smékal, Zdeněk (referee) ; Kiska, Tomáš (advisor)
This work deals with the analysis of parameters related to the dynamics of sound recordings. It contains a brief description of the history of sound processing in analogue and digital form and the process of audio signal processing nowadays. The following chapter includes selection of the most suitable parameters for describing an audio recording, especially those describing the dynamics. This work further characterizes the methods used in similar researches in the world. There is also a system designed to calculate 43 dynamic parameters and the possibilities of their analysis are outlined as well. 35 different interpretations of one musical work were compared. Finally, the calculated parameters were drawn into scatter plots and evaluated using visual cluster analysis.
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Efficient implementation of methods for the restoration of damaged audio signals
Csiba, Hajnalka ; Rajmic, Pavel (referee) ; Mokrý, Ondřej (advisor)
This bachelor's thesis deals with the restoration of audio signals containing unknown samples at known locations using two algorithms. The first is the Janssen algorithm and the second is a method based on non-negative matrix factorization. Janssen algorithm is built on the principle of the autoregressive model. The restoration of the samples is performed in such a way that the restored signal matches the predicted model as precisely as possible. The algorithm based on non-negative matrix factorization is used to decompose the frequency spectrogram of the signal as the product of non-negative matrices.
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Deep learning based sound records analysis
Kramář, Denis ; Říha, Kamil (referee) ; Přinosil, Jiří (advisor)
This master thesis deals with the problem of audio-classification of the chainsaw logging sound in natural environment using mainly convolutional neural networks. First, a theory of grafical representation of audio signal is discussed. Following part is devoted to the machine learning area. In third chapter, some of present works dealing with this problematics are given. Within the practical part, used dataset and tested neural networks are presented. Final resultes are compared by achieved accuracy and by ROC curves. The robustness of the presented solutions was tested by proposed detection program and evaluated using objective criteria.
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Restoration of damaged audio signals using autoregressive models
Soboňa, Matúš ; Rajmic, Pavel (referee) ; Mokrý, Ondřej (advisor)
The bachelor thesis deals with the problem of restoring audio signals damaged by sample loss, using autoregressive models. The restoration itself is solved by W. Etter and A. Janssen's algorithms. These algorithms are implemented in MATLAB and tested on artificial signals aswell as on real recordings. Algorithms are then compared based on quality of restoration dependent on different parameters of signals.
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Restoration of audio signals damaged by quantization
Šiška, Jakub ; Rajmic, Pavel (referee) ; Záviška, Pavel (advisor)
This master’s thesis deals with the restoration of audio signals damaged by quantization. The theoretical part starts with a description of quantization and dequantization in general, few existing methods of dequantization of audio signals and theory of sparse representations of signals are also presented. The next part introduces algorithms suitable for dequantization, specifically Douglas–Rachford, Chambolle–Pock, SPADEQ and implementation of these algorithms in MATLAB application in the next chapter. In the last part of this thesis, testing of reconstructed signals using the algorithms takes place and results are evaluated by objective measures SDR, PEMO-Q, PEAQ and subjective listening test MUSHRA.
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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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