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Musical genre classification
Káčerová, Erika ; Říha, Kamil (referee) ; Uher, Václav (advisor)
The aim of this bachelor thesis is creating a system for automatic music genre recognition. The thesis deals with two main issues, which are feature extraction of a genre and machine learning process. For the purpose of feature extraction a source code is written in JAVA programming language based on jAudio library. Six machine learning models are created in RapidMiner Studio software. The most appropriate one of them, Neural Networks method is then improved and tested on different parts of songs from database.These database contains 250 training songs and 25 test songs from five music genres: classical music, disco, drum and bass, hip hop and rock.
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Estimation of formant frequencies using machine learning
Káčerová, Erika ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
This Master's thesis deals with the issue of formant extraction. A system of scripts in Matlab interface is created to generate values of the first three formant frequencies from speech recordings with the use of Praat and Snack(WaveSurfer). Mel Frequency Cepstral Coefficients and Linear Predictive Coefficients are extracted from the audio files in order to be added to the database. This database is then used to train a neural network. Finally, the designed neural network is tested.
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Estimation of formant frequencies using machine learning
Káčerová, Erika ; Galáž, Zoltán (referee) ; Mekyska, Jiří (advisor)
This Master's thesis deals with the issue of formant extraction. A system of scripts in Matlab interface is created to generate values of the first three formant frequencies from speech recordings with the use of Praat and Snack(WaveSurfer). Mel Frequency Cepstral Coefficients and Linear Predictive Coefficients are extracted from the audio files in order to be added to the database. This database is then used to train a neural network. Finally, the designed neural network is tested.
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Musical genre classification
Káčerová, Erika ; Říha, Kamil (referee) ; Uher, Václav (advisor)
The aim of this bachelor thesis is creating a system for automatic music genre recognition. The thesis deals with two main issues, which are feature extraction of a genre and machine learning process. For the purpose of feature extraction a source code is written in JAVA programming language based on jAudio library. Six machine learning models are created in RapidMiner Studio software. The most appropriate one of them, Neural Networks method is then improved and tested on different parts of songs from database.These database contains 250 training songs and 25 test songs from five music genres: classical music, disco, drum and bass, hip hop and rock.
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