|
|
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.
|
|
|
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.
|
|
|
Web interface for audio feature visualization
Putz, Viliam ; Ištvánek, Matěj (referee) ; Miklánek, Štěpán (advisor)
This thesis deals with methods of audio features extraction from audio files, visualization of these features and implementation of web interface, which provides the visualization. In the introduction, Music Information Retrieval field, with which this thesis is closely related, is described. Also, the current state in the area of applications for audio features extraction is described. Next, the most common libraries for audio feature extraction within the programming languages are listed. In the second chapter, the audio features that can be extracted from audio file are listed and described. In the third chapter, there is description of the process of implementation, used technologies, function diagram of the web interface, explanation of functionality and description of user interface and its functions.
|
|
|
Web interface for audio feature visualization
Putz, Viliam ; Ištvánek, Matěj (referee) ; Miklánek, Štěpán (advisor)
This thesis deals with methods of audio features extraction from audio files, visualization of these features and implementation of web interface, which provides the visualization. In the introduction, Music Information Retrieval field, with which this thesis is closely related, is described. Also, the current state in the area of applications for audio features extraction is described. Next, the most common libraries for audio feature extraction within the programming languages are listed. In the second chapter, the audio features that can be extracted from audio file are listed and described. In the third chapter, there is description of the process of implementation, used technologies, function diagram of the web interface, explanation of functionality and description of user interface and its functions.
|
|
|
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.
|
|
|
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.
|
guest ::
