Araştırma Makalesi
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Emotion Recognition From Different Types of Music From Different Cultures

Yıl 2020, Cilt: 35 Sayı: 3, 687 - 698, 30.09.2020
https://doi.org/10.21605/cukurovaummfd.846661

Öz

In this study, various machine learning methods were used to recognize emotions on databases of different types of music belonging to different cultures. In order to obtain features from the music in these databases, widely used toolboxes were preferred. Correlation-based feature selection method was applied to all the obtained features. BayesNet, Sequential Minimal Optimization, Logistic Regression and Decision Trees are used as machine learning methods. When BayesNet was applied to the remaining features after the feature selection process, %94,35 recognition accuracy rate was obtained for Turkish Emotional Music Database, %79,62 for Bi-Modal Database, and %75,45 for Soundtrack Database, and better results were achieved than other classifiers. Then, the features obtained from the toolboxes were combined and the selection process was made again. After this process, recognition rates of %95,96, %80,24 and %82,72 were obtained for these databases, respectively.

Kaynakça

  • 1. Alakuş, T.B., Türkoğlu, İ., 2018. EEG Tabanlı Duygu Analiz Sistemleri. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 11(1), 26-39.
  • 2. Kaya, A.İ., 2018. Üniversite Öğrencilerinde Dini İçerikli Müzik Terapinin Kaygı, Duygu Durumu ve Algılanan Stres Üzerindeki Etkisi. Uluslararası Din & Felsefe Araştırmaları Dergisi 1,1.
  • 3. Erdal, B., 2015. Hissedilen ve Algılanan Duygular Bağlamında Arabesk Müzik Beğenisi Etkileyen Faktörler Üzerine Bir Araştırma. International Journal of Human Sciences, 12, 1: 1016-1055 doi: 10.14687/ijhs.v12i1.3199.
  • 4. Hevner, K., 1936. Experimental Studies of the Elements of Expression in Music. The American Journal of Psychology, 48(2), 246-268.
  • 5. Feng, Y., Zhuang, Y., Pan, Y., 2003. Popular Music Retrieval by Detecting Mood. In: SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), 375–376.
  • 6. Ekman, P., 2005. Basic Emotions. In Handbook of Cognition and Emotion, (3), 45-60, doi: 10.1002/0470013494.ch3.
  • 7. Russell, J.A., 1980. A Circumplex Model of Affect. Journal of Personality and Social Psychology, 39, 1161–1178.
  • 8. Thayer, R.E., 1989. The Biopsychology of Mood and Arousal. New York: Oxford University Press.
  • 9. Yang, Y.H., Chen, H.H., 2011. Ranking-based Emotion Recognition for Music Organization and Retrieval. IEEE Transactions on Audio Speech and Language Processing, 19, 762–774.
  • 10. Soleymani, M., Caro, M.N., Schmidt, E.M., Sha, C.Y., Yang, Y.H., 2013. 1000 Songs for Emotional Analysis of Music. CrowdMM- Proceedings of the 2nd ACM International Workshop on Crowdsourcing for Multimedia; 1-6, doi:10.1145/2506364.2506365.
  • 11. Lartillot, O., Toiviainen, P., 2007. Mir in Matlab (II): A Toolbox for Musical Feature Extraction from Audio. Proceedings of the 8th International Conference on Music Information Retrieval, September 23-27, Vienna, Austria, 127–130.
  • 12. Cabrera, D., 1999. PsySound: A Computer Program for Psychoacoustical Analysis. Australian Acoustical Society Conference, 47-54, doi: 10.1002/asi.
  • 13. McKay, C., 2009. JAudio: Towards a Standardized Extensible Audio Music Feature Extraction System. Course Paper, McGill University, Canada.
  • 14. Eyben, F., Schuller, B., 2015. OpenSMILE– The Munich Versatile and Fast Open-source Audio Feature Extractor. ACM SIGMultimedia Records, 6(4), 4–13, doi: 10.1145- 2729095.2729097.
  • 15. Tzanetakis, G., Cook, P., 1999. MARSYAS: A Framework for Audio Analysis. Organised Sound, 4(3), 169-175.
  • 16. Song, Y., Dixon, S., Pearce, M., 2012. Evaluation of Musical Features for Emotion Classification. Proceedings of the 13th International Society for Music Information Retrieval Conference, October, Porto, Portugal.
  • 17. Kim, Y., Schmidt, E., Migneco, R., Morton, B., Richardson, P., Scott, J., Speck, J., Turnbull, D., 2010. State of the Art Report: Music Emotion Recognition: A State of the Art Review. Proceedings of the 11th International Society for Music Information Retrieval Conference, 9-13 August, Utrecht, Netherlands. 255-266.
  • 18. Rocha, B., Panda, R., Paiva, R.P., 2013. Music Emotion Recognition: The Importance of Melodic Features. 6th International Workshop on Music and Machine Learning in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September, Prague, Czech Republic.
  • 19. Delbouys, R., Hennequin, R., Piccoli, F., Royo-Letelier, J. ve Moussallam, M., 2018. Music Mood Detection Based on Audio and Lyrics with Deep Neural Net. CoRR, abs/1809.07276.
  • 20. Zhang, F., Meng, H., Li, M., 2016. Emotion Extraction and Recognition from Music. 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 1728-1733, doi: 10.1109- FSKD.2016.7603438.
  • 21. Grekow, J., 2015. Audio Features Dedicated to the Detection of Four Basic Emotion. Computer Information Systems and Industrial Management: CISIM’2015: 14th IFIP TC8 International Conference, September 24-26, Warszawa, Poland.
  • 22. Benito-Gorron, D.de., Lozano-Diez, A., Toledano, D.T., Gonzalez- Rodriguez, J., 2019. Exploring Convolutional, Recurrent, and Hybrid Deep Neural Networks for Speech and Music Detection in a Large Audio Dataset. Eurasip Journal on Audio, Speech, and Music Processing, (1), 1–18.
  • 23. Sarkar, R., Choudhury, S., Dutta, S., Roy, A., Saha, S.K., 2019. Recognition of Emotion in Music Based on Deep Convolutional Neural Network. Multimedia Tools and Applications, 79, 765–783.
  • 24. Hızlısoy, S., Tüfekci, Z., 2020. Türkçe Müzikten Duygu Tanıma. 1st International Conference on Computer, Electrical, and Electronic Sciences, 8-10 October 2020.
  • 25. Yang, Y.H., Su, Y.F., Lin, Y.C., Chen, H.H., 2007. Music Emotion Recognition: The Role of Individuality. In Proceedings of the ACM International Workshop on Human-Centered Multimedia, 13-21.
  • 26. Malheiro, R., Panda, R., Gomes, P., Paiva, R.P., 2016. Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset. 9th International Workshop on Music and Machine Learning-MML’2016-in Conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases- ECML/PKDD, Riva del Garda, Italy.
  • 27. Eerola, T., Vuoskoski, J., 2011. A Comparison of the Discrete and Dimensional Models of Emotion in Music. Psychology of Music, 10.1177/0305735610362821.
  • 28. Hall, M., Smith, L., 1997. Feature Subset Selection: A Correlation-based Filter Approach. Proceedings of the 4th International Conference on Neural Information Processing and Intelligent Information Systems, New Zealand, 855–858.
  • 29. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I., 2008. The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl.. 11. 10-18.
  • 30. Quinlan, J.R., 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
  • 31. Pearl, J., 1985. Bayesian Networks: A Model of Self-activated Memory for Evidential Reasoning. Proceedings of the Seventh Conference of the Cognitive Science Society, California, USA.
  • 32. Platt, J., 1998. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Microsoft Research Technical Report: MSRTR, 98-14.
  • 33. Le Cessie, S., van Houwelingen, J.C., 1992. Ridge Estimators in Logistic Regression. Applied Statistics, 41(1), 191-201.

Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma

Yıl 2020, Cilt: 35 Sayı: 3, 687 - 698, 30.09.2020
https://doi.org/10.21605/cukurovaummfd.846661

Öz

Bu çalışmada, klasik makine öğrenme yöntemleri farklı kültürlere ait farklı türdeki müziklerden oluşmuş veri tabanları üzerinde duygu tanıması yapmak için kullanılmışlardır. Bu veri tabanlarında bulunan müziklerden öznitelik çıkarmak için çalışmalarda yaygın olarak kullanılan araçlar tercih edilmiştir. Çıkarılan bütün özniteliklere korelasyon tabanlı öznitelik seçme yöntemi uygulanmıştır. Makine öğrenmesi yöntemleri olarak Bayes Ağları, Sıralı Minimal Optimizasyon, Lojistik Regresyon ve Karar Ağaçları kullanılmıştır. Öznitelik seçim işlemi sonrasında kalan özniteliklere Bayes Ağları yöntemi uygulandığında, Türkçe Duygusal Müzik Veri Tabanı için %94,35, Bi-Modal Veri Tabanı için %79,62 ve Soundtrack Veri Tabanı için ise %75,45 tanıma oranı elde edilmiş ve karşılaştırılan sınıflandırıcılardan daha iyi sonuç alınmıştır. Daha sonra, araçlardan çıkarılan öznitelikler bir araya getirilmiş ve yine seçim işlemi yapılmıştır. Bu işlemden sonra ise, sırasıyla bu veritabanları için %95,96, %80,24 ve %82,72 tanıma oranları elde edilmiştir.

Kaynakça

  • 1. Alakuş, T.B., Türkoğlu, İ., 2018. EEG Tabanlı Duygu Analiz Sistemleri. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 11(1), 26-39.
  • 2. Kaya, A.İ., 2018. Üniversite Öğrencilerinde Dini İçerikli Müzik Terapinin Kaygı, Duygu Durumu ve Algılanan Stres Üzerindeki Etkisi. Uluslararası Din & Felsefe Araştırmaları Dergisi 1,1.
  • 3. Erdal, B., 2015. Hissedilen ve Algılanan Duygular Bağlamında Arabesk Müzik Beğenisi Etkileyen Faktörler Üzerine Bir Araştırma. International Journal of Human Sciences, 12, 1: 1016-1055 doi: 10.14687/ijhs.v12i1.3199.
  • 4. Hevner, K., 1936. Experimental Studies of the Elements of Expression in Music. The American Journal of Psychology, 48(2), 246-268.
  • 5. Feng, Y., Zhuang, Y., Pan, Y., 2003. Popular Music Retrieval by Detecting Mood. In: SIGIR Forum (ACM Spec. Interes. Gr. Inf. Retrieval), 375–376.
  • 6. Ekman, P., 2005. Basic Emotions. In Handbook of Cognition and Emotion, (3), 45-60, doi: 10.1002/0470013494.ch3.
  • 7. Russell, J.A., 1980. A Circumplex Model of Affect. Journal of Personality and Social Psychology, 39, 1161–1178.
  • 8. Thayer, R.E., 1989. The Biopsychology of Mood and Arousal. New York: Oxford University Press.
  • 9. Yang, Y.H., Chen, H.H., 2011. Ranking-based Emotion Recognition for Music Organization and Retrieval. IEEE Transactions on Audio Speech and Language Processing, 19, 762–774.
  • 10. Soleymani, M., Caro, M.N., Schmidt, E.M., Sha, C.Y., Yang, Y.H., 2013. 1000 Songs for Emotional Analysis of Music. CrowdMM- Proceedings of the 2nd ACM International Workshop on Crowdsourcing for Multimedia; 1-6, doi:10.1145/2506364.2506365.
  • 11. Lartillot, O., Toiviainen, P., 2007. Mir in Matlab (II): A Toolbox for Musical Feature Extraction from Audio. Proceedings of the 8th International Conference on Music Information Retrieval, September 23-27, Vienna, Austria, 127–130.
  • 12. Cabrera, D., 1999. PsySound: A Computer Program for Psychoacoustical Analysis. Australian Acoustical Society Conference, 47-54, doi: 10.1002/asi.
  • 13. McKay, C., 2009. JAudio: Towards a Standardized Extensible Audio Music Feature Extraction System. Course Paper, McGill University, Canada.
  • 14. Eyben, F., Schuller, B., 2015. OpenSMILE– The Munich Versatile and Fast Open-source Audio Feature Extractor. ACM SIGMultimedia Records, 6(4), 4–13, doi: 10.1145- 2729095.2729097.
  • 15. Tzanetakis, G., Cook, P., 1999. MARSYAS: A Framework for Audio Analysis. Organised Sound, 4(3), 169-175.
  • 16. Song, Y., Dixon, S., Pearce, M., 2012. Evaluation of Musical Features for Emotion Classification. Proceedings of the 13th International Society for Music Information Retrieval Conference, October, Porto, Portugal.
  • 17. Kim, Y., Schmidt, E., Migneco, R., Morton, B., Richardson, P., Scott, J., Speck, J., Turnbull, D., 2010. State of the Art Report: Music Emotion Recognition: A State of the Art Review. Proceedings of the 11th International Society for Music Information Retrieval Conference, 9-13 August, Utrecht, Netherlands. 255-266.
  • 18. Rocha, B., Panda, R., Paiva, R.P., 2013. Music Emotion Recognition: The Importance of Melodic Features. 6th International Workshop on Music and Machine Learning in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September, Prague, Czech Republic.
  • 19. Delbouys, R., Hennequin, R., Piccoli, F., Royo-Letelier, J. ve Moussallam, M., 2018. Music Mood Detection Based on Audio and Lyrics with Deep Neural Net. CoRR, abs/1809.07276.
  • 20. Zhang, F., Meng, H., Li, M., 2016. Emotion Extraction and Recognition from Music. 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 1728-1733, doi: 10.1109- FSKD.2016.7603438.
  • 21. Grekow, J., 2015. Audio Features Dedicated to the Detection of Four Basic Emotion. Computer Information Systems and Industrial Management: CISIM’2015: 14th IFIP TC8 International Conference, September 24-26, Warszawa, Poland.
  • 22. Benito-Gorron, D.de., Lozano-Diez, A., Toledano, D.T., Gonzalez- Rodriguez, J., 2019. Exploring Convolutional, Recurrent, and Hybrid Deep Neural Networks for Speech and Music Detection in a Large Audio Dataset. Eurasip Journal on Audio, Speech, and Music Processing, (1), 1–18.
  • 23. Sarkar, R., Choudhury, S., Dutta, S., Roy, A., Saha, S.K., 2019. Recognition of Emotion in Music Based on Deep Convolutional Neural Network. Multimedia Tools and Applications, 79, 765–783.
  • 24. Hızlısoy, S., Tüfekci, Z., 2020. Türkçe Müzikten Duygu Tanıma. 1st International Conference on Computer, Electrical, and Electronic Sciences, 8-10 October 2020.
  • 25. Yang, Y.H., Su, Y.F., Lin, Y.C., Chen, H.H., 2007. Music Emotion Recognition: The Role of Individuality. In Proceedings of the ACM International Workshop on Human-Centered Multimedia, 13-21.
  • 26. Malheiro, R., Panda, R., Gomes, P., Paiva, R.P., 2016. Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset. 9th International Workshop on Music and Machine Learning-MML’2016-in Conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases- ECML/PKDD, Riva del Garda, Italy.
  • 27. Eerola, T., Vuoskoski, J., 2011. A Comparison of the Discrete and Dimensional Models of Emotion in Music. Psychology of Music, 10.1177/0305735610362821.
  • 28. Hall, M., Smith, L., 1997. Feature Subset Selection: A Correlation-based Filter Approach. Proceedings of the 4th International Conference on Neural Information Processing and Intelligent Information Systems, New Zealand, 855–858.
  • 29. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I., 2008. The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl.. 11. 10-18.
  • 30. Quinlan, J.R., 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers.
  • 31. Pearl, J., 1985. Bayesian Networks: A Model of Self-activated Memory for Evidential Reasoning. Proceedings of the Seventh Conference of the Cognitive Science Society, California, USA.
  • 32. Platt, J., 1998. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Microsoft Research Technical Report: MSRTR, 98-14.
  • 33. Le Cessie, S., van Houwelingen, J.C., 1992. Ridge Estimators in Logistic Regression. Applied Statistics, 41(1), 191-201.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Serhat Hızlısoy

Zekeriya Tüfekci Bu kişi benim

Yayımlanma Tarihi 30 Eylül 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 35 Sayı: 3

Kaynak Göster

APA Hızlısoy, S., & Tüfekci, Z. (2020). Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(3), 687-698. https://doi.org/10.21605/cukurovaummfd.846661
AMA Hızlısoy S, Tüfekci Z. Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma. cukurovaummfd. Eylül 2020;35(3):687-698. doi:10.21605/cukurovaummfd.846661
Chicago Hızlısoy, Serhat, ve Zekeriya Tüfekci. “Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35, sy. 3 (Eylül 2020): 687-98. https://doi.org/10.21605/cukurovaummfd.846661.
EndNote Hızlısoy S, Tüfekci Z (01 Eylül 2020) Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35 3 687–698.
IEEE S. Hızlısoy ve Z. Tüfekci, “Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma”, cukurovaummfd, c. 35, sy. 3, ss. 687–698, 2020, doi: 10.21605/cukurovaummfd.846661.
ISNAD Hızlısoy, Serhat - Tüfekci, Zekeriya. “Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35/3 (Eylül 2020), 687-698. https://doi.org/10.21605/cukurovaummfd.846661.
JAMA Hızlısoy S, Tüfekci Z. Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma. cukurovaummfd. 2020;35:687–698.
MLA Hızlısoy, Serhat ve Zekeriya Tüfekci. “Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 35, sy. 3, 2020, ss. 687-98, doi:10.21605/cukurovaummfd.846661.
Vancouver Hızlısoy S, Tüfekci Z. Farklı Kültürlere Ait Farklı Türdeki Müziklerden Duygu Tanıma. cukurovaummfd. 2020;35(3):687-98.