Research Article
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Genres Classification of Popular Songs Listening by Using Keras

Year 2024, Volume: 11 Issue: 1, 123 - 136, 28.03.2024

İlhan Tarımer Buse Cennet Karadağ

https://doi.org/10.54287/gujsa.1374878

Abstract

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 TensorFlow/Keras. 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.

Keywords

Musical Genre MFCC Keras Classification

Supporting Institution

Muğla sıtkı Koçman University

Thanks

We, as the authors, would like to thank to Muğla Sıtkı Koçman University for giving us suitable environment to realize this study.

References

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  • Chen, Y., Guo, Q., Liang, X., Wang, J., Qian, Y. (2019). Environmental Sound Classification with Dilated Convolutions, Elsevier Applied Acoustics. 148(1). 123-132, https://dig.sxu.edu.cn/docs/2019-03/2d147cee7 cfb4f3eb25ee73f6e6dd3de.pdf
  • Chillara, S., Kavitha, A. S., Neginhal, S. A., Haldia, S., Vidyullatha, K. S. (2019). Music Genre Classification Using Machine Learning Algorithms: A Comparison. International Research Journal of Engineering and Technology, 6(5), 851-858, https://www.irjet.net/archives/V6/i5/IRJET-V6I5174.pdf
  • Davis, N., Suresh, K. (2018). Environmental sound classification using deep convolutional neural networks and data augmentation, IEEE Recent Advances in Intelligent Computational Systems (RAICS), (pp. 41-45). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8635051&tag=1
  • Demir, F., Türkoğlu, M., Aslan, M., Sengur, A. (2020). A New Pyramidal Concatenated CNN Approach For Environmental Sound Classification, Elsevier, 170(2020), 7. https://www.sciencedirect.com/science/article/ pii/S0003682X20306241
  • Eray, O. (2008). The speech recognition application with support vector machines, Pamukkale University, (pp. 1-90). https://gcris.pau.edu.tr/handle/11499/1501
  • Gessle, G., & Åkesson, S. (2019). A comparative analysis of CNN and LSTM for music genre classification, Degree Project In Technology 2022 https://www.diva-ortal.org/smash/get/diva2:1354738/FULLTEXT01.pdf
  • Ghildiyal, A., Singh, K., Sharma, S. (2020). Music genre classification using machine learning, International Conference on Electronics, Communication and Aerospace Technology (ICECA), (pp. 1368-1372). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9297444
  • Harte, C., Sandler, M., Gasser, M. (2006). Detecting harmonic change in musical audio, 1st ACM Workshop on Audio and Music Computing Multimedia (AMCMM), (pp. 21-26). https://dl.acm.org/doi/ 10.1145/1178723.1178727
  • Hussain, A., Mkpojiogu, E., Almazini, H., Almazini, H. (2019). Assessing the usability of Shazam mobile app, The 2nd Internatİonal Conference on Applied Science and Technology (ICAST’17), (pp. 0200571– 0200575). https://pubs.aip.org/aip/acp/article/1891/1/020057/886329/Assessing-the-usability-of-Shazam-mobile-app
  • İmik, U., Haşhaş, S. (2020). What is Music and Where Is It In Our Lives?, İnönü University Journal of Culture and Art, 6(2), 196-202, https://dergipark.org.tr/en/download/article-file/1491772
  • Karhan, M., Çakır, M., Uğur, M. (2016). Analysis of Electrical Discharge Sound Data Using MEL Frequency Cepstral Coefficients (MFCC), In Proceeding of the 2016 cigrė Türkiye, (pp. 4). http://gsk.cigreturkiye.org.tr/bildiriler2016/5.2.pdf
  • Kattel, M., Nepal, A., Shah, A. K., & Shrestha, D. (2019). Chroma feature extraction, Conference: Chroma Feature Extraction Using Fourier Transform, (pp 1-4). https://www.researchgate.net/publication/330796993_ Chroma_Feature_Extraction
  • Nirmal M. R., & Shajee Mohan B. S. (2020). Music Genre Classification using spectrograms, International Conference on Power, Instrumentation, Control and Computing (PICC), (pp. 1-5). https://ieeexplore.ieee.org/document/9362364
  • Pelchat, N., & Gelowitz, C. M. (2020). Neural Network Music Genre Classification, Canadian Journal of Electrical and Computer Engineering, 43(3), 170-173. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=& arnumber=9165253
  • Patil, N. M., Nemade, & M. U. (2017). Music Genre Classification Using MFCC, K-NN and SVM Classifier, International Journal of Computer Engineering In Research Trends, 4(2), 43-47. https://www.ijcert.org/index.php/ijcert/article/view/371/313
  • Salamon, J., & Bello, J. P. (2017). Deep convolutional neural networks and data augmentation for environmental sound classification, IEEE Signal Processing Letters, (pp. 279-283). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7829341
  • Yang, H., & Zhang, W. Q. (2019). Music genre classification using duplicated convolutional layers in neural networks, INTERSPEECH, (pp. 3382-3386). https://www.isca-archive.org/interspeech_2019/yang19f_ interspeech.html
  • Yıldırım, M. (2022). Automatic Classification of Environmental Sounds with the MFCC Method and the Proposed Deep Model, Fırat University Journal of Engineering Sciences, 34(1), 449-457. https://dergipark.org.tr/en/download/article-file/2186156
  • Tzanetakis, G., Cook, P. (2020). Musical Genre Classification of Audio Signals, IEEE Transactions on Speech and Audio Processing, 10(5), 293-302. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1021072
  • Vishnupriya, S., & Meenakshi, K. (2018). Automatic music genre classification using convolution neural network, International Conference on Computer Communication and Informatics (ICCCI), (pp. 1-4.). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8441340
  • Web Source 1: (Shazam Nasıl Çalışıyor?). (2024). (Accessesd: 12.02.2024) https://musiconline.co/tr/blog/ shazam-nasil-calisiyor
  • Web Source 2: (Music genre classification using Librosa and Tensorflow/Keras). (2024). (Accessesd: 12.02.2024) https://musiconline.co/tr/blog/shazam-nasil-calisiyor
  • Web Source 3: (Librosa). (2024). (Accessesd: 12.02.2024) https://medium.com/datarunner/librosa-9729c09ecf7a
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Year 2024, Volume: 11 Issue: 1, 123 - 136, 28.03.2024

İlhan Tarımer Buse Cennet Karadağ

https://doi.org/10.54287/gujsa.1374878

Abstract

References

  • Bahuleyan, H. (2018). Music Genre Classification Using Machine Learning Techniques, ArXiv:1804.01149v1, 1(1). https://doi.org/10.48550/arXiv.1804.01149
  • Chen, Y., Guo, Q., Liang, X., Wang, J., Qian, Y. (2019). Environmental Sound Classification with Dilated Convolutions, Elsevier Applied Acoustics. 148(1). 123-132, https://dig.sxu.edu.cn/docs/2019-03/2d147cee7 cfb4f3eb25ee73f6e6dd3de.pdf
  • Chillara, S., Kavitha, A. S., Neginhal, S. A., Haldia, S., Vidyullatha, K. S. (2019). Music Genre Classification Using Machine Learning Algorithms: A Comparison. International Research Journal of Engineering and Technology, 6(5), 851-858, https://www.irjet.net/archives/V6/i5/IRJET-V6I5174.pdf
  • Davis, N., Suresh, K. (2018). Environmental sound classification using deep convolutional neural networks and data augmentation, IEEE Recent Advances in Intelligent Computational Systems (RAICS), (pp. 41-45). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8635051&tag=1
  • Demir, F., Türkoğlu, M., Aslan, M., Sengur, A. (2020). A New Pyramidal Concatenated CNN Approach For Environmental Sound Classification, Elsevier, 170(2020), 7. https://www.sciencedirect.com/science/article/ pii/S0003682X20306241
  • Eray, O. (2008). The speech recognition application with support vector machines, Pamukkale University, (pp. 1-90). https://gcris.pau.edu.tr/handle/11499/1501
  • Gessle, G., & Åkesson, S. (2019). A comparative analysis of CNN and LSTM for music genre classification, Degree Project In Technology 2022 https://www.diva-ortal.org/smash/get/diva2:1354738/FULLTEXT01.pdf
  • Ghildiyal, A., Singh, K., Sharma, S. (2020). Music genre classification using machine learning, International Conference on Electronics, Communication and Aerospace Technology (ICECA), (pp. 1368-1372). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9297444
  • Harte, C., Sandler, M., Gasser, M. (2006). Detecting harmonic change in musical audio, 1st ACM Workshop on Audio and Music Computing Multimedia (AMCMM), (pp. 21-26). https://dl.acm.org/doi/ 10.1145/1178723.1178727
  • Hussain, A., Mkpojiogu, E., Almazini, H., Almazini, H. (2019). Assessing the usability of Shazam mobile app, The 2nd Internatİonal Conference on Applied Science and Technology (ICAST’17), (pp. 0200571– 0200575). https://pubs.aip.org/aip/acp/article/1891/1/020057/886329/Assessing-the-usability-of-Shazam-mobile-app
  • İmik, U., Haşhaş, S. (2020). What is Music and Where Is It In Our Lives?, İnönü University Journal of Culture and Art, 6(2), 196-202, https://dergipark.org.tr/en/download/article-file/1491772
  • Karhan, M., Çakır, M., Uğur, M. (2016). Analysis of Electrical Discharge Sound Data Using MEL Frequency Cepstral Coefficients (MFCC), In Proceeding of the 2016 cigrė Türkiye, (pp. 4). http://gsk.cigreturkiye.org.tr/bildiriler2016/5.2.pdf
  • Kattel, M., Nepal, A., Shah, A. K., & Shrestha, D. (2019). Chroma feature extraction, Conference: Chroma Feature Extraction Using Fourier Transform, (pp 1-4). https://www.researchgate.net/publication/330796993_ Chroma_Feature_Extraction
  • Nirmal M. R., & Shajee Mohan B. S. (2020). Music Genre Classification using spectrograms, International Conference on Power, Instrumentation, Control and Computing (PICC), (pp. 1-5). https://ieeexplore.ieee.org/document/9362364
  • Pelchat, N., & Gelowitz, C. M. (2020). Neural Network Music Genre Classification, Canadian Journal of Electrical and Computer Engineering, 43(3), 170-173. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=& arnumber=9165253
  • Patil, N. M., Nemade, & M. U. (2017). Music Genre Classification Using MFCC, K-NN and SVM Classifier, International Journal of Computer Engineering In Research Trends, 4(2), 43-47. https://www.ijcert.org/index.php/ijcert/article/view/371/313
  • Salamon, J., & Bello, J. P. (2017). Deep convolutional neural networks and data augmentation for environmental sound classification, IEEE Signal Processing Letters, (pp. 279-283). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7829341
  • Yang, H., & Zhang, W. Q. (2019). Music genre classification using duplicated convolutional layers in neural networks, INTERSPEECH, (pp. 3382-3386). https://www.isca-archive.org/interspeech_2019/yang19f_ interspeech.html
  • Yıldırım, M. (2022). Automatic Classification of Environmental Sounds with the MFCC Method and the Proposed Deep Model, Fırat University Journal of Engineering Sciences, 34(1), 449-457. https://dergipark.org.tr/en/download/article-file/2186156
  • Tzanetakis, G., Cook, P. (2020). Musical Genre Classification of Audio Signals, IEEE Transactions on Speech and Audio Processing, 10(5), 293-302. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1021072
  • Vishnupriya, S., & Meenakshi, K. (2018). Automatic music genre classification using convolution neural network, International Conference on Computer Communication and Informatics (ICCCI), (pp. 1-4.). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8441340
  • Web Source 1: (Shazam Nasıl Çalışıyor?). (2024). (Accessesd: 12.02.2024) https://musiconline.co/tr/blog/ shazam-nasil-calisiyor
  • Web Source 2: (Music genre classification using Librosa and Tensorflow/Keras). (2024). (Accessesd: 12.02.2024) https://musiconline.co/tr/blog/shazam-nasil-calisiyor
  • Web Source 3: (Librosa). (2024). (Accessesd: 12.02.2024) https://medium.com/datarunner/librosa-9729c09ecf7a
  • Web Source 4: (librosa.feature.tonnetz). (2024). (Accessesd: 12.02.2024) https://librosa.org/doc/main/ generated/librosa.feature.tonnetz.html
  • Zhang, S., Gu, H., & Li, R. (2019). Music Genre Classification: Near-Realtime vs Sequential Approach, PrePrint. (pp. 6). https://cs229.stanford.edu/proj2019spr/report/3.pdf
There are 26 citations in total.

Details

Primary Language English
Subjects Audio Processing
Journal Section Electronics, Sensors and Digital Hardware
Authors

İlhan Tarımer 0000-0002-7274-5680

Buse Cennet Karadağ 0000-0002-2488-1047

Early Pub Date February 15, 2024
Publication Date March 28, 2024
Submission Date October 12, 2023
Acceptance Date February 8, 2024
Published in Issue Year 2024 Volume: 11 Issue: 1

Cite

APA Tarımer, İ., & Karadağ, B. C. (2024). Genres Classification of Popular Songs Listening by Using Keras. Gazi University Journal of Science Part A: Engineering and Innovation, 11(1), 123-136. https://doi.org/10.54287/gujsa.1374878
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