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

In 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. (C) 2020 Karabuk University. Publishing services by Elsevier B.V.

Automated summary

An approach for music emotion recognition based on CLDNN architecture is proposed, with a new Turkish emotional music database constructed for evaluation. The method shows significant improvement in accuracy using feature combination and LSTM + DNN classifier, indicating its potential in music emotion recognition.