Music emotion recognition using deep neural networks

Music has an economic value of billions of dollars today. In order to correctly detect the emotion felt in music, correct classification is required. In this study, we try to present a comprehensive research on music emotion recognition (MER) using deep neural networks in order to increase the accuracy of emotion detection and classification from music. Although many studies have been conducted on the music of different countries, there are very few studies on Turkish music. Therefore, we developed our study using a dataset consisting of Turkish songs. In our research, we used deep learning architectures (CNN, LSTM) and machine learning algorithm (RFC) to discover the relationships between various sound features, melody, harmony, rhythm, complex patterns and the emotions triggered by these features. Our first goal in our study was to produce a more stable dataset during the model development phase by making modifications on a signal basis. After improving our model to an acceptable level of accuracy, our ultimate goal was to simplify the model to a level that would require less workload. The LibROSA library was used to characterize sound features. To increase the robustness and generalization ability of the model across different music genres, data augmentation strategies using Gaussian noise and low-pass filters were used. Focusing on the performance of the models, we tried to demonstrate their effectiveness in predicting emotional states such as happiness, sadness, anger and relaxation in music files in our dataset. With the data augmentation strategies we used, we managed to significantly increase the model performance in terms of both accuracy and efficiency. In addition, we observed that the incompatibility problems that can be encountered during the processing of different audio files were completely eliminated. In summary, we believe that this study not only provides many technical contributions to the field of music emotion recognition, but also provides outputs that can support future research at the intersection of technology, psychology and musicology.