A comprehensive study of classification of music genres with voice analysis

Yıl 2024, Cilt: 13 Sayı: 1, 325 - 333, 15.01.2024

Zekeriya Anıl Güven

https://doi.org/10.28948/ngumuh.1344605

Öz

Voice analysis is a type of analysis that can be applied in many areas such as disease detection, emotion analysis, and classification of music genres. Classification can be made by applying image or data analysis according to the problem with voice characteristics. In this study, the classification of music genres was performed by extracting the voice characteristics. GTZAN, which consists of 10 music genre tags, was used as the dataset. In the analysis phase, the effects of audio segmentation and feature selection methods on the classification of music genres were investigated. Machine learning methods and deep neural networks were used for classification. In the first stage of the analysis, the accuracy value increased by 10.58% by applying only the audio segmentation. As a result of this study, after the application of audio segmentation and feature selection methods, Deep Neural Network provided an increase of 14.19% with an accuracy of 95.19%.

Anahtar Kelimeler

Voice Analysis, Machine Learning, Deep Neural Networks, Feature Selection, Audio Segmentation

Ses analizi ile müzik türlerinin sınıflandırılmasına yönelik kapsamlı bir çalışma

Öz

Ses analizi; hastalık tespiti, duygu analizi, müzik türleri sınıflandırma gibi birçok alanda uygulanabilen bir analiz türüdür. Ses karakteristik özellikleri ile probleme göre görüntü analizi, veri analizi uygulanarak sınıflandırma yapılabilmektedir. Bu çalışmada, ses karakteristik özellikleri çıkarılarak müzik türlerinin sınıflandırması gerçekleştirilmiştir. Veri seti olarak 10 müzik türü etiketinden oluşan GTZAN kullanılmıştır. Analiz aşamasında, ses bölütleme işlemi ve özellik seçme yöntemlerinin de müzik türünü sınıflandırmaya etkisi araştırılmıştır. Sınıflandırma için makine öğrenme yöntemleri ve derin sinir ağlarından yararlanılmıştır. Analizin ilk aşamasında, sadece ses bölütleme işlemi uygulanmasıyla doğruluk değeri %10.58 artış göstermiştir. Çalışmanın sonucunda, ses bölütleme işlemi ve özellik seçme yöntemleri uygulanması sonrasında öğrenme yöntemlerinden Derin Sinir Ağları yöntemi %95.19 doğruluk değeriyle %14.19 başarı artışı sağlamıştır.

Anahtar Kelimeler

Ses Analizi, Makine Öğrenmesi, Derin Sinir Ağları, Özellik Seçme, Ses Bölütleme

Kaynakça

• M. B. Gulmezoglu, V. Dzhafarov, M. Keskin, and A. Barkana, A novel approach to isolated word recognition. IEEE Transactions on Speech and Audio Processing, 7(6), 620-628, 1999. https://doi.org/10.1109/89.799687
• S. Keser, Improvement of face recognition performance using a new hybrid subspace classifier. Signal, Image and Video Processing, 17(5), 2511-2520, 2023.
• N. Pelchat and C. M. Gelowitz, Neural network music genre classification. Canadian Journal of Electrical and Computer Engineering, 43(3), 170-173, 2020. https://doi.org/10.1109/CJECE.2020.2970144
• A. Elbir and N. Aydin, Music genre classification and music recommendation by using deep learning. Electronics Letters, 56(12), 627-629, 2020. https://doi.org/10.1049/el.2019.4202
• A. Ghildiyal, K. Singh and S. Sharma, Music genre classification using machine learning. In 2020 4th international conference on electronics, communication and aerospace technology (ICECA), pp. 1368-1372, India, 5-7 November 2020.
• C. Senac, T. Pellegrini, F. Mouret and J. Pinquier, Music feature maps with convolutional neural networks for music genre classification. In Proceedings of the 15th international workshop on content-based multimedia indexing, 1-5, Florence, Italy, 19-21 June 2017. https://doi.org/10.1145/3095713.3095733
• H. Bahuleyan, Music genre classification using machine learning techniques. arXiv preprint, 2018. arXiv:1804.01149.
• Y. H. Cheng, P. C. Chang and C. N. Kuo, Convolutional neural networks approach for music genre classification. In 2020 International Symposium on Computer, Consumer and Control (IS3C), 399-403, Taiwan, 13-16 November 2020. https://doi.org/10.1109/IS3C50286.2020.00109
• L. Nanni, Y. M. Costa, D. R. Lucio, C. N. Silla Jr, and S. Brahnam, Combining visual and acoustic features for audio classification tasks. Pattern Recognition Letters, 88, 49-56, 2017. https://doi.org/10.1016/j.patrec.2017.01.013
• P. Barros, C. Weber, and S. Wermter, Learning auditory neural representations for emotion recognition. In 2016 International Joint Conference on Neural Networks (IJCNN), 921-928, July 2016.
• S. K. Prabhakar, and S. W. Lee, Holistic approaches to music genre classification using efficient transfer and deep learning techniques. Expert Systems with Applications, 211, 118636, 2023. https://doi.org/10.1016/j.eswa.2022.118636
• Y. Panagakis, and C. Kotropoulos, Music genre classification via topology preserving non-negative tensor factorization and sparse representations. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 249-252, March 2010.
• C. Bakir, and M. Yuzkat, Speech emotion classification and recognition with different methods for Turkish language. Balkan Journal of Electrical and Computer Engineering, 6(2), 122-128, 2018. https://doi.org/10.17694/bajece.419557
• M. Sharma, S. Joshi, T. Chatterjee, and R. Hamid, A comprehensive empirical review of modern voice activity detection approaches for movies and TV shows. Neurocomputing, 494, 116-131, 2022. https://doi.org/10.1016/j.neucom.2022.04.084
• O. Karaman, H. Çakın, A. Alhudhaif and K. Polat, Robust automated Parkinson disease detection based on voice signals with transfer learning. Expert Systems with Applications, 178, 115013, 2021. https://doi.org/10.1016/j.eswa.2021.115013
• C. Quan, K. Ren and Z. Luo, A deep learning based method for Parkinson’s disease detection using dynamic features of speech. IEEE Access, 9, 10239-10252, 2021. https://doi.org/10.1109/ACCESS.2021.3051432
• O. Amir, W.T. Abraham, Z. S. Azzam, G. Berger, S. D. Anker, S. P. Pinney, D. Burkhoff, I. D. Shallom, C. Lotan and E. R. Edelman, Remote speech analysis in the evaluation of hospitalized patients with acute decompensated heart failure. Heart Failure, 10(1), 41-49, 2022. https://doi.org/10.1016/j.jchf.2021.08.008
• Z. Al-Jumaili, T. Bassiouny, A. Alanezi, W. Khan, D. Al-Jumeily and A. J., Hussain, Classification of spoken English accents using deep learning and speech analysis. In International Conference on Intelligent Computing, 277-287, Xi'an, China, 7-11 August 2022.
• O. F. Çıplak, ve S. Keser, Gerçek Zamanlı Ses Tanıma ile Robot Kolu Kontrolü. Avrupa Bilim Ve Teknoloji Dergisi, (31), 34-39, 2021. https://doi.org/10.31590/ejosat.969608
• G. Tzanetakis, and P. Cook, Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5), 293-302, 2002. https://doi.org/10.1109/TSA.2002.800560
• B. McFee, C. Raffel, D. Liang, D. P. Ellis, M. McVicar, E. Battenberg and O. Nieto, librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, 18-25, Texas, (8), 6-12 July 2015.
• C. J. Steinmetz, and J. Reiss, pyloudnorm: A simple yet flexible loudness meter in Python. In Audio Engineering Society Convention 150. Audio Engineering Society, 2021.
• J. Han, M., Kamber and J. Pei, Data preprocessing. Data Mining (Third Edition). The Morgan Kaufmann Series in Data Management Systems, pp. 83-124, 2012.
• L. V. Filter, and P. Filter, Seven techniques for dimensionality reduction. Technical report, 2014.
• A. Sikri, N. P. Singh, and S. Dalal, Chi-Square Method of feature selection: impact of pre-processing of data. International Journal of Intelligent Systems and Applications in Engineering, 11(3s), 241-248, 2023.
• I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines. Machine learning, 46, 389-422, 2002.
• Ö. Şahinaslan, H. Dalyan, ve E. Şahinaslan. Naive bayes sınıflandırıcısı kullanılarak youtube verileri üzerinden çok dilli duygu analizi. Bilişim Teknolojileri Dergisi, 15 (2), 221-229, 2022. http://doi.org/10.17671/gazibtd.999960
• E. Çavuş ve İ. Sancaktar, Batarya sağlık durumunun makine öğrenmesi ile kestirimi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11 (3), 601-610, 2022. https://doi.org/10.28948/ngumuh.1112985.
• N. Akyel ve K. Seçkin. K-en Yakin Komşuluk Algori̇tmasının Hi̇le Deneti̇mi̇nde Kullanımı. Journal of Accounting and Taxation Studies, 5(1), 21-40, 2012.
• A. C. Başara ve Y. Şişman, Frekans oranı, kanıt ağırlığı ve lojistik regresyon yöntemleri kullanılarak heyelan duyarlılık haritalarının CBS tabanlı karşılaştırılması. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 11 (3), 647-660, 2022. https://doi.org/10.28948/ngumuh.1065284
• A. C. Kelle ve H. Yüce, MQTT Trafiğinde DoS Saldırılarının Makine Öğrenmesi ile Sınıflandırılması ve Modelin SHAP ile Yorumlanması. Journal of Materials and Mechatronics: A, 3(1), 50-62, 2022.
• M. Açıkkar, Prediction Of Gross Calorific Value Of Coal From Proximate And Ultimate Analysis Variables Using Support Vector Machines With Feature Selection. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 9 (2), 1129-1141, 2020. https://doi.org/10.28948/ngumuh.585596.
• G. Montavon, W. Samek, and K. R. Müller, Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, 1-15, 2018.