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Speech-signal-based recognition of type of transmission channel
Kopřiva, Tomáš ; Burget, Radim (referee) ; Atassi, Hicham (advisor)
This work deals with the classification of five different transmission channels by speech signal processing. The channels considered are: GSM, two PSTN channels and two VoIP channels. For the training and testing purposes, a speech database for the transmission channels called SPLAB_TranCh was constructed. The speech signals of this corpus originally come from well-known TIMIT database, where each utterance passed through each mentioned transmission channel. The main objective of this work is to find optimal features and classification accuracy that yield best classification accuracy. Several types of features, including MFCC, LPCC and spectral characteristics were put under examination. The best suprasegmental features were identified by using mRMR algorithm. Several classifiers were tested as well. The results suggested that the classification of transmission channel can be performed with high accuracy (around 90 %). Influence of adverse effects, which can occur during transmission, is also examined. Considered types of distortions are: saturation, thresholding, echo, crackling noises and different colors of noises and filters.
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Parkinson disease diagnosis using speech signal analysis
Karásek, Michal ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
The thesis deals with the recognition of Parkinson's disease from the speech signal. The first part refers to the principles of speech signals and speech signals by patients suffering from Parkinson's disease. Further, it continues to describe the issues of speech signals processing, basic symptoms used for diagnosis of Parkinson's disease (e. g. VAI, VSA, FCR, VOT etc.) and reduction of these symptoms. The next part focuses on a block diagram of the program for the diagnosis of Parkinson's disease. The main objective of this thesis is comparison of two methods of feature selection (mRMR and SFFS). For classification have selected two different methods were used. The first method is classification kNN and second method of classification is Gaussian mixture model (GMM).
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Recognition of music cover versions using Music Information Retrieval techniques
Martinek, Václav ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
This master’s thesis deals with designs and implementation of systems for music cover recognition. The introduction part is devoted to the calculation parameters from audio signal using Music Information Retrieval techniques. Subsequently, various forms of cover versions and musical aspects that cover versions share are defined. The thesis also deals in detail with the creation and distribution of a database of cover versions. Furthermore, the work presents methods and techniques for comparing and processing the calculated parameters. Attention is then paid to the OTI method, CSM calculation and methods dealing with parameter selection. The next part of the thesis is devoted to the design of systems for recognizing cover versions. Then there are compared systems already designed for recognizing cover versions. Furthermore, the thesis describes machine learning techniques and evaluation methods for evaluating the classification with a special emphasis on artificial neural networks. The last part of the thesis deals with the implementation of two systems in MATLAB and Python. These systems are then tested on the created database of cover versions.
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Diagnosing Parkinson's disease from analysis of speech recording
Vymlátil, Petr ; Trzos, Michal (referee) ; Lněnička, Jakub (advisor)
This thesis is focused on diagnosing Parkinson’s disease from analysis of speech recording. Introduction of this work deals with description of voice production mechanism, it’s basic qualities and influence of hypokinetic dysarthria on speech. In next chapter, there is described voice signal and some methods of it’s preprocessing. Next part continues dealing with description of chosen individual symptoms, which are needed for PD diagnosing, followed by definition of chosen reduction methods and classifiers. There is a comparison of classify succes of naive bayes classifier, depending on chosen reduction method in last chapter of this work.
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Recognition of music cover versions using Music Information Retrieval techniques
Martinek, Václav ; Zvončák, Vojtěch (referee) ; Kiska, Tomáš (advisor)
This master’s thesis deals with designs and implementation of systems for music cover recognition. The introduction part is devoted to the calculation parameters from audio signal using Music Information Retrieval techniques. Subsequently, various forms of cover versions and musical aspects that cover versions share are defined. The thesis also deals in detail with the creation and distribution of a database of cover versions. Furthermore, the work presents methods and techniques for comparing and processing the calculated parameters. Attention is then paid to the OTI method, CSM calculation and methods dealing with parameter selection. The next part of the thesis is devoted to the design of systems for recognizing cover versions. Then there are compared systems already designed for recognizing cover versions. Furthermore, the thesis describes machine learning techniques and evaluation methods for evaluating the classification with a special emphasis on artificial neural networks. The last part of the thesis deals with the implementation of two systems in MATLAB and Python. These systems are then tested on the created database of cover versions.
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Diagnosing Parkinson's disease from analysis of speech recording
Vymlátil, Petr ; Trzos, Michal (referee) ; Lněnička, Jakub (advisor)
This thesis is focused on diagnosing Parkinson’s disease from analysis of speech recording. Introduction of this work deals with description of voice production mechanism, it’s basic qualities and influence of hypokinetic dysarthria on speech. In next chapter, there is described voice signal and some methods of it’s preprocessing. Next part continues dealing with description of chosen individual symptoms, which are needed for PD diagnosing, followed by definition of chosen reduction methods and classifiers. There is a comparison of classify succes of naive bayes classifier, depending on chosen reduction method in last chapter of this work.
|
|
|
Speech-signal-based recognition of type of transmission channel
Kopřiva, Tomáš ; Burget, Radim (referee) ; Atassi, Hicham (advisor)
This work deals with the classification of five different transmission channels by speech signal processing. The channels considered are: GSM, two PSTN channels and two VoIP channels. For the training and testing purposes, a speech database for the transmission channels called SPLAB_TranCh was constructed. The speech signals of this corpus originally come from well-known TIMIT database, where each utterance passed through each mentioned transmission channel. The main objective of this work is to find optimal features and classification accuracy that yield best classification accuracy. Several types of features, including MFCC, LPCC and spectral characteristics were put under examination. The best suprasegmental features were identified by using mRMR algorithm. Several classifiers were tested as well. The results suggested that the classification of transmission channel can be performed with high accuracy (around 90 %). Influence of adverse effects, which can occur during transmission, is also examined. Considered types of distortions are: saturation, thresholding, echo, crackling noises and different colors of noises and filters.
|
|
|
Parkinson disease diagnosis using speech signal analysis
Karásek, Michal ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
The thesis deals with the recognition of Parkinson's disease from the speech signal. The first part refers to the principles of speech signals and speech signals by patients suffering from Parkinson's disease. Further, it continues to describe the issues of speech signals processing, basic symptoms used for diagnosis of Parkinson's disease (e. g. VAI, VSA, FCR, VOT etc.) and reduction of these symptoms. The next part focuses on a block diagram of the program for the diagnosis of Parkinson's disease. The main objective of this thesis is comparison of two methods of feature selection (mRMR and SFFS). For classification have selected two different methods were used. The first method is classification kNN and second method of classification is Gaussian mixture model (GMM).
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