Araştırma Makalesi
BibTex RIS Kaynak Göster

Classification of Emotion with Audio Analysis

Yıl 2023, Cilt: 28 Sayı: 2, 467 - 481, 31.08.2023
https://doi.org/10.53433/yyufbed.1219879

Öz

Classification is an important technique used to distinguish data samples. The aim of this study is to classify according to emotions by extracting audio features. Two male and two female individuals expressed four different emotions as "fun", "angry", "neutral" and "sleepy" in the voice data. We used to “MFCC” as a Cepstral feature, “Centroid, Flatness, Skewness, Crest, Flux, Slope, Decrease, Kurtosis, Spread, Entropy, roll off point” as Spectral Feature, “Pitch, Harmonic ratio” as Periodicity Features in the sound features. After, we applied to the data that all the classification algorithms located in the classification learner toolbox in Matlab and we tried to classify the emotion with the algorithm that provides the highest accuracy. Each data in the classification study has twenty-six features inputs and one labeled output value. According to the results, support vector machine algorithm provided the highest accuracy performance. Considering the performances obtained, this study reveals that it is possible to distinguish and classify sounds using sentimental data and sound feature parameters.

Kaynakça

  • Adigwe, A., Tits, N., Haddad, K. E., Ostadabbas, S., & Dutoit, T. (2018). The emotional voices database: Towards controlling the emotion dimension in voice generation systems. arXiv preprint arXiv:1806.09514. doi:10.48550/arXiv.1806.09514
  • Antoni, J. (2006). The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20(2), 282-307. doi:10.1016/j.ymssp.2004.09.001
  • Aouani, H., & Ayed, Y. B. (2018, March). Emotion recognition in speech using MFCC with SVM, DSVM and auto-encoder. 2018 4th International conference on advanced technologies for signal and image processing (ATSIP), Sousse, Tunisia. doi:10.1109/ATSIP.2018.8364518
  • Chatterjee, J., Mukesh, V., Hsu, H.-H., Vyas, G., & Liu, Z. (2018, August). Speech emotion recognition using cross-correlation and acoustic features. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/ PiCom/ DataCom/ CyberSciTech), Athens, Greece. doi:10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00050
  • Dubnov, S. (2004). Generalization of spectral flatness measure for non-gaussian linear processes. IEEE Signal Processing Letters, 11(8), 698-701. doi:10.1109/LSP.2004.831663
  • Eskidere, Ö., & Ertaş, F. (2009). Mel frekansı kepstrum katsayılarındaki değişimlerin konuşmacı tanımaya etkisi. Uludağ University Journal of The Faculty of Engineering, 14(2), 93-110.
  • Giannakopoulos, T. & Pikrakis, A. (2014). Introduction to audio analysis: A MATLAB® approach. Orlando, FL, USA: Academic Press Inc.
  • Giannoulis, D., Massberg, M. & Reiss, J. D. (2013). Parameter automation in a dynamic range compressor. Journal of the Audio Engineering Society, 61(10), 716-726.
  • Grey, J. M., & Gordon, J. W. (1978). Perceptual effects of spectral modifications on musical timbres. The Journal of the Acoustical Society of America, 63(5), 1493-1500. doi:10.1121/1.381843
  • Jain, U., Nathani, K., Ruban, N., Raj, A. N. J., Zhuang, Z., & Mahesh, V. G. V. (2018, October). Cubic SVM classifier based feature extraction and emotion detection from speech signals. 2018 International Conference on Sensor Networks and Signal Processing (SNSP), Xi'an, China. doi:10.1109/SNSP.2018.00081
  • Kaynar, O., Görmez, Y., Yıldız, M., & Albayrak, A. (2016, September). Makine öğrenmesi yöntemleri ile duygu analizi. International Artificial Intelligence and Data Processing Symposium (IDAP'16), Malatya, Türkiye.
  • Kishore, B., Yasar, A., Taspinar, Y. S., Kursun, R., Cinar, I., Shankar, V. G., … & Ofori, I. (2022). Computer-aided multiclass classification of corn from corn images integrating deep feature extraction. Computational Intelligence and Neuroscience, 2022, 2062944. doi:10.1155/2022/2062944
  • Koolagudi, S. G., Maity, S., Kumar, V. A., Chakrabarti, S., & Rao, K. S. (2009). IITKGP-SESC: Speech Database for Emotion Analysis. In S. Ranka et al. (Eds). Contemporary Computing: Second International Conference (pp. 485-492). Noida, India: Springer Berlin Heidelberg. doi:10.1007/978-3-642-03547-0_46
  • Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica (Slovenia), 31(3), 249-268.
  • Krüger, F. (2016). Activity, context, and plan recognition with computational causal behaviour models. (PhD), University of Rostock, Institute of Communications Engineering, Rostock, Germany.
  • Lech, M., Stolar, M., Best, C., & Bolia, R. (2020). Real-time speech emotion recognition using a pre-trained image classification network: Effects of bandwidth reduction and companding. Frontiers in Computer Science, 2, 14. doi:10.3389/fcomp.2020.00014
  • Lerch, A. (2012). An introduction to audio content analysis: Applications in signal processing and music informatics. New Jersey, USA: Wiley-IEEE Press.
  • Metlek, S., & Kayaalp, K., 2020. Makine Öğrenmesinde, Teoriden Örnek MATLAB Uygulamalarına Kadar Destek Vektör Makineleri. Ankara, Türkiye: İksad Yayınevi.
  • Milton, A., Roy, S. S., & Selvi, S. T. (2013). SVM scheme for speech emotion recognition using MFCC feature. International Journal of Computer Applications, 69(9), 34-39. doi:10.5120/11872-7667
  • Misra, H., Ikbal, S., Bourlard, H., & Hermansky, H. (2004, May). Spectral entropy based feature for robust ASR. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QC, Canada. doi:10.1109/ICASSP.2004.1325955
  • Mitrović, D., Zeppelzauer, M., & Breiteneder, C. (2010). Chapter 3- Features for content-based audio retrieval. In M. V. Zelkowitz (Ed.), Advances in Computers, Vol. 78 (pp. 71-150). Burlington, USA: Elsevier. doi:10.1016/S0065-2458(10)78003-7
  • Mohamad Nezami, O., Jamshid Lou, P., & Karami, M. (2019). ShEMO: a large-scale validated database for Persian speech emotion detection. Language Resources and Evaluation, 53, 1-16. doi:10.1007/s10579-018-9427-x
  • Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project. CUIDADO Ist Project Report (pp. 1-25). Paris, France: Icram.
  • Peeters, G., Giordano, B. L., Susini, P., Misdariis, N., & McAdams, S. (2011). The timbre toolbox: Extracting audio descriptors from musical signals. The Journal of the Acoustical Society of America, 130(5), 2902-2916. doi:10.1121/1.3642604
  • Rebala, G., Ravi, A., & Churiwala, S. (2019). An Introduction to Machine Learning. Cham, Switzerland: Springer.
  • Sonawane, A., Inamdar, M. U., & Bhangale, K. B. (2017, August). Sound based human emotion recognition using MFCC & multiple SVM. 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), Indore, India. doi:10.1109/ICOMICON.2017.8279046
  • Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. doi:10.1016/j.aci.2018.08.003
  • Tuncer, T., Dogan, S., & Acharya, U. R. (2021). Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques. Knowledge-Based Systems, 211, 106547. doi:10.1016/j.knosys.2020.106547
  • Vyas, G., & Kumari, B. (2013). Speaker recognition system based on mfcc and dct. International Journal of Engineering and Advanced Technology (IJEAT), 2(5), 167-169.
  • Yasar, A., Saritas, I., & Korkmaz, H. (2018). Determination of intestinal mass by region growing method. Preprints, 2018, 2018050449. doi:10.20944/preprints201805.0449.v1
  • Yasar, A. (2022). Benchmarking analysis of CNN models for bread wheat varieties. European Food Research and Technology, 249, 749-758. doi:10.1007/s00217-022-04172-y

Ses Analiziyle Duyguların Sınıflandırılması

Yıl 2023, Cilt: 28 Sayı: 2, 467 - 481, 31.08.2023
https://doi.org/10.53433/yyufbed.1219879

Öz

Sınıflandırma, veri örneklerini ayırt edebilmek için kullanılan önemli bir tekniktir. Bu çalışmada öz nitelikler çıkartılarak, duygulara göre sesin sınıflandırılması amaçlanmıştır. Neşeli, sinirli, nötr ve uykulu olmak üzere dört farklı duyguda konuşan iki erkek ve iki kadın bireyden alınan ses verileri kullanılmıştır. Sesin özniteliklerinde; Kepstral özellik olarak “Mel-Frekansı Kepstral Katsayıları”, Spektral Özellik olarak “Ağırlık Merkezi, Pürüzsüzlük, Çarpıklık, Tepe, Akış, Eğim, Azalma, Basıklık, Yayılma, Entropi, Yuvarlanma noktası”, Periyodisite Özelliği olarak “Ses perdesi, Harmonik oran” kullandık. Daha sonra, Matlab’da bulunan “sınıflandırma öğrenici” araç kutusunda yer alan tüm sınıflandırma algoritmalarını veriye uyguladık ve en yüksek doğruluğu sağlayan algoritmayla duyguyu tahmin etmeye çalıştık. Sınıflandırma çalışmasında yer alan her bir veri, yirmi altı öz nitelik girdisi ve bir etiketli çıktı değerine sahiptir. Performans sonuçlarına göre, destek vektör makine algoritması en yüksek doğruluk değerini sağlamıştır. Elde edilen performans çıktıları göz önüne alındığında, bu çalışma, duyusal veriler ve ses öznitelikleri kullanılarak sesleri ayırt etmenin ve sınıflandırmanın mümkün olduğunu ortaya koymaktadır.

Kaynakça

  • Adigwe, A., Tits, N., Haddad, K. E., Ostadabbas, S., & Dutoit, T. (2018). The emotional voices database: Towards controlling the emotion dimension in voice generation systems. arXiv preprint arXiv:1806.09514. doi:10.48550/arXiv.1806.09514
  • Antoni, J. (2006). The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20(2), 282-307. doi:10.1016/j.ymssp.2004.09.001
  • Aouani, H., & Ayed, Y. B. (2018, March). Emotion recognition in speech using MFCC with SVM, DSVM and auto-encoder. 2018 4th International conference on advanced technologies for signal and image processing (ATSIP), Sousse, Tunisia. doi:10.1109/ATSIP.2018.8364518
  • Chatterjee, J., Mukesh, V., Hsu, H.-H., Vyas, G., & Liu, Z. (2018, August). Speech emotion recognition using cross-correlation and acoustic features. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/ PiCom/ DataCom/ CyberSciTech), Athens, Greece. doi:10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00050
  • Dubnov, S. (2004). Generalization of spectral flatness measure for non-gaussian linear processes. IEEE Signal Processing Letters, 11(8), 698-701. doi:10.1109/LSP.2004.831663
  • Eskidere, Ö., & Ertaş, F. (2009). Mel frekansı kepstrum katsayılarındaki değişimlerin konuşmacı tanımaya etkisi. Uludağ University Journal of The Faculty of Engineering, 14(2), 93-110.
  • Giannakopoulos, T. & Pikrakis, A. (2014). Introduction to audio analysis: A MATLAB® approach. Orlando, FL, USA: Academic Press Inc.
  • Giannoulis, D., Massberg, M. & Reiss, J. D. (2013). Parameter automation in a dynamic range compressor. Journal of the Audio Engineering Society, 61(10), 716-726.
  • Grey, J. M., & Gordon, J. W. (1978). Perceptual effects of spectral modifications on musical timbres. The Journal of the Acoustical Society of America, 63(5), 1493-1500. doi:10.1121/1.381843
  • Jain, U., Nathani, K., Ruban, N., Raj, A. N. J., Zhuang, Z., & Mahesh, V. G. V. (2018, October). Cubic SVM classifier based feature extraction and emotion detection from speech signals. 2018 International Conference on Sensor Networks and Signal Processing (SNSP), Xi'an, China. doi:10.1109/SNSP.2018.00081
  • Kaynar, O., Görmez, Y., Yıldız, M., & Albayrak, A. (2016, September). Makine öğrenmesi yöntemleri ile duygu analizi. International Artificial Intelligence and Data Processing Symposium (IDAP'16), Malatya, Türkiye.
  • Kishore, B., Yasar, A., Taspinar, Y. S., Kursun, R., Cinar, I., Shankar, V. G., … & Ofori, I. (2022). Computer-aided multiclass classification of corn from corn images integrating deep feature extraction. Computational Intelligence and Neuroscience, 2022, 2062944. doi:10.1155/2022/2062944
  • Koolagudi, S. G., Maity, S., Kumar, V. A., Chakrabarti, S., & Rao, K. S. (2009). IITKGP-SESC: Speech Database for Emotion Analysis. In S. Ranka et al. (Eds). Contemporary Computing: Second International Conference (pp. 485-492). Noida, India: Springer Berlin Heidelberg. doi:10.1007/978-3-642-03547-0_46
  • Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica (Slovenia), 31(3), 249-268.
  • Krüger, F. (2016). Activity, context, and plan recognition with computational causal behaviour models. (PhD), University of Rostock, Institute of Communications Engineering, Rostock, Germany.
  • Lech, M., Stolar, M., Best, C., & Bolia, R. (2020). Real-time speech emotion recognition using a pre-trained image classification network: Effects of bandwidth reduction and companding. Frontiers in Computer Science, 2, 14. doi:10.3389/fcomp.2020.00014
  • Lerch, A. (2012). An introduction to audio content analysis: Applications in signal processing and music informatics. New Jersey, USA: Wiley-IEEE Press.
  • Metlek, S., & Kayaalp, K., 2020. Makine Öğrenmesinde, Teoriden Örnek MATLAB Uygulamalarına Kadar Destek Vektör Makineleri. Ankara, Türkiye: İksad Yayınevi.
  • Milton, A., Roy, S. S., & Selvi, S. T. (2013). SVM scheme for speech emotion recognition using MFCC feature. International Journal of Computer Applications, 69(9), 34-39. doi:10.5120/11872-7667
  • Misra, H., Ikbal, S., Bourlard, H., & Hermansky, H. (2004, May). Spectral entropy based feature for robust ASR. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, QC, Canada. doi:10.1109/ICASSP.2004.1325955
  • Mitrović, D., Zeppelzauer, M., & Breiteneder, C. (2010). Chapter 3- Features for content-based audio retrieval. In M. V. Zelkowitz (Ed.), Advances in Computers, Vol. 78 (pp. 71-150). Burlington, USA: Elsevier. doi:10.1016/S0065-2458(10)78003-7
  • Mohamad Nezami, O., Jamshid Lou, P., & Karami, M. (2019). ShEMO: a large-scale validated database for Persian speech emotion detection. Language Resources and Evaluation, 53, 1-16. doi:10.1007/s10579-018-9427-x
  • Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project. CUIDADO Ist Project Report (pp. 1-25). Paris, France: Icram.
  • Peeters, G., Giordano, B. L., Susini, P., Misdariis, N., & McAdams, S. (2011). The timbre toolbox: Extracting audio descriptors from musical signals. The Journal of the Acoustical Society of America, 130(5), 2902-2916. doi:10.1121/1.3642604
  • Rebala, G., Ravi, A., & Churiwala, S. (2019). An Introduction to Machine Learning. Cham, Switzerland: Springer.
  • Sonawane, A., Inamdar, M. U., & Bhangale, K. B. (2017, August). Sound based human emotion recognition using MFCC & multiple SVM. 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), Indore, India. doi:10.1109/ICOMICON.2017.8279046
  • Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. doi:10.1016/j.aci.2018.08.003
  • Tuncer, T., Dogan, S., & Acharya, U. R. (2021). Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques. Knowledge-Based Systems, 211, 106547. doi:10.1016/j.knosys.2020.106547
  • Vyas, G., & Kumari, B. (2013). Speaker recognition system based on mfcc and dct. International Journal of Engineering and Advanced Technology (IJEAT), 2(5), 167-169.
  • Yasar, A., Saritas, I., & Korkmaz, H. (2018). Determination of intestinal mass by region growing method. Preprints, 2018, 2018050449. doi:10.20944/preprints201805.0449.v1
  • Yasar, A. (2022). Benchmarking analysis of CNN models for bread wheat varieties. European Food Research and Technology, 249, 749-758. doi:10.1007/s00217-022-04172-y
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Mühendislik ve Mimarlık / Engineering and Architecture
Yazarlar

Coşkucan Büyükyıldız 0000-0002-8190-5914

Ismail Sarıtas 0000-0002-5743-4593

Ali Yaşar 0000-0001-9012-7950

Yayımlanma Tarihi 31 Ağustos 2023
Gönderilme Tarihi 16 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 28 Sayı: 2

Kaynak Göster

APA Büyükyıldız, C., Sarıtas, I., & Yaşar, A. (2023). Classification of Emotion with Audio Analysis. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 28(2), 467-481. https://doi.org/10.53433/yyufbed.1219879