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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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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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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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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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