Music emotion recognition using convolutional long short term memory deep neural networks

HIZLISOY S., Yildirim S., TÜFEKCİ Z.

Engineering Science and Technology, an International Journal, cilt.24, ss.760-767, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.jestch.2020.10.009
  • Dergi Adı: Engineering Science and Technology, an International Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.760-767
  • Anahtar Kelimeler: Music emotion recognition, Convolutional long short term memory deep neural networks, Turkish emotional music database, SPEECH, REGRESSION
  • Kayseri Üniversitesi Adresli: Evet

Özet

© 2020 Karabuk UniversityIn this paper, we propose an approach for music emotion recognition based on convolutional long short term memory deep neural network (CLDNN) architecture. In addition, we construct a new Turkish emotional music database composed of 124 Turkish traditional music excerpts with a duration of 30 s each and the performance of the proposed approach is evaluated on the constructed database. We utilize features obtained by feeding convolutional neural network (CNN) layers with log-mel filterbank energies and mel frequency cepstral coefficients (MFCCs) in addition to standard acoustic features. Classification results show that the best performance is obtained when the new feature set is combined with the standard features using the long short term memory (LSTM) + deep neural network (DNN) classi fier. The overall accuracy of 99.19% is obtained using the proposed system with 10 fold cross-validation. Specifically, 6.45 points improvement is achieved. Additionally, the results also show that the LSTM + DNN classifier yields 1.61, 1.61 and 3.23 points improvements in music emotion recognition accuracies compared to k-nearest neighbor (k-NN), support vector machine (SVM), and Random Forest classifiers, respectively.