Genres Classification of Popular Songs Listening by Using Keras

İlhan Tarımer; Buse Cennet Karadağ

Makale detayları

Genres Classification of Popular Songs Listening by Using Keras

Yazarlar:

İlhan Tarımer | Buse Cennet Karadağ

Yayıncı:

Gazi University

Ana Başlıklar:

Audio Processing

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 1, pp.123-136

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001310698

pISSN:

2147-9542

Anahtar kelimeler:

Özet:

 Listening to the music affects the brain in ways which might help to promote the human health and arrange various diseases symptoms. Music is a phenomenon that is intertwined at every stage of human life. In the modern era music is shaped by the combination of an incredible number of genres, some of which are contemporary, and some come from the previous times. The music genre represents a collection of musical works that develop according to a certain shape, expression and technique. The music genre of interest varies from person to person in society. Most listeners today do not know what kind of music they listen to. In this study, sound features were extracted from music data and the Keras model was trained using these attributes. The correct classification rate of a music genre of the trained model was determined as 71.66. Mel Frequency Cepstral Coefficients MFCC, Mel Spectrogram, Chroma Vector and Tonnetz methods in the Librosa library were used to extract sound properties from music data. Using the features probed by means of the library, the most listened songs with Shazam in Türkiye were categorized in with TensorFlowKeras. Many methods can be used in classification. It is uncertain which method the researchers should opt. It has been emphasized that classification of the genres of newly released songs by using Keras in this study. At result, it is said that the study has presented a sound processing are Keras classification of musical parts.

 Listening to the music affects the brain in ways which might help to promote the human health and arrange various diseases symptoms. Music is a phenomenon that is intertwined at every stage of human life. In the modern era music is shaped by the combination of an incredible number of genres, some of which are contemporary, and some come from the previous times. The music genre represents a collection of musical works that develop according to a certain shape, expression and technique. The music genre of interest varies from person to person in society. Most listeners today do not know what kind of music they listen to. In this study, sound features were extracted from music data and the Keras model was trained using these attributes. The correct classification rate of a music genre of the trained model was determined as 71.66. Mel Frequency Cepstral Coefficients MFCC, Mel Spectrogram, Chroma Vector and Tonnetz methods in the Librosa library were used to extract sound properties from music data. Using the features probed by means of the library, the most listened songs with Shazam in Türkiye were categorized in with TensorFlowKeras. Many methods can be used in classification. It is uncertain which method the researchers should opt. It has been emphasized that classification of the genres of newly released songs by using Keras in this study. At result, it is said that the study has presented a sound processing are Keras classification of musical parts.

 Listening to the music affects the brain in ways which might help to promote the human health and arrange various diseases symptoms. Music is a phenomenon that is intertwined at every stage of human life. In the modern era music is shaped by the combination of an incredible number of genres, some of which are contemporary, and some come from the previous times. The music genre represents a collection of musical works that develop according to a certain shape, expression and technique. The music genre of interest varies from person to person in society. Most listeners today do not know what kind of music they listen to. In this study, sound features were extracted from music data and the Keras model was trained using these attributes. The correct classification rate of a music genre of the trained model was determined as 71.66. Mel Frequency Cepstral Coefficients MFCC, Mel Spectrogram, Chroma Vector and Tonnetz methods in the Librosa library were used to extract sound properties from music data. Using the features probed by means of the library, the most listened songs with Shazam in Türkiye were categorized in with TensorFlowKeras. Many methods can be used in classification. It is uncertain which method the researchers should opt. It has been emphasized that classification of the genres of newly released songs by using Keras in this study. At result, it is said that the study has presented a sound processing are Keras classification of musical parts.

 Listening to the music affects the brain in ways which might help to promote the human health and arrange various diseases symptoms. Music is a phenomenon that is intertwined at every stage of human life. In the modern era music is shaped by the combination of an incredible number of genres, some of which are contemporary, and some come from the previous times. The music genre represents a collection of musical works that develop according to a certain shape, expression and technique. The music genre of interest varies from person to person in society. Most listeners today do not know what kind of music they listen to. In this study, sound features were extracted from music data and the Keras model was trained using these attributes. The correct classification rate of a music genre of the trained model was determined as 71.66. Mel Frequency Cepstral Coefficients MFCC, Mel Spectrogram, Chroma Vector and Tonnetz methods in the Librosa library were used to extract sound properties from music data. Using the features probed by means of the library, the most listened songs with Shazam in Türkiye were categorized in with TensorFlowKeras. Many methods can be used in classification. It is uncertain which method the researchers should opt. It has been emphasized that classification of the genres of newly released songs by using Keras in this study. At result, it is said that the study has presented a sound processing are Keras classification of musical parts.

Makale detayları

Genres Classification of Popular Songs Listening by Using Keras

Yazarlar:

İlhan Tarımer | Buse Cennet Karadağ

Yayıncı:

Gazi University

Ana Başlıklar:

Audio Processing

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 1, pp.123-136

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001310698

pISSN:

2147-9542

Musical Genre

MFCC

Keras

Classification

Sayı

Makale detayları

Genres Classification of Popular Songs Listening by Using Keras

Yazarlar: İlhan Tarımer | Buse Cennet Karadağ

Yayıncı: Gazi University

Ana Başlıklar: Audio Processing

Dergi: Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 1, pp.123-136

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001310698

pISSN: 2147-9542

Musical Genre

MFCC

Keras

Classification

Sayı

Yazarlar:

İlhan Tarımer | Buse Cennet Karadağ

Yayıncı:

Gazi University

Ana Başlıklar:

Audio Processing

Dergi:

Gazi University Journal of Science Part A: Engineering and Innovation Vol.11 Issue 1, pp.123-136

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001310698

pISSN:

2147-9542