A Deep Attentive Network for Music Emotion Recognition using the chorus part of the music

Abstract

In the domain of music information retrieval, music emotion recognition (MER) is an active area of research. The chorus part of the music is the most critical and emotional section. In this paper, we proposed an MER approach that combines Convolutional Neural Networks with Long Short-Term Memory to extract emotional features from the log-mel spectrogram of the music chorus part. Then, the features feed into an attention mechanism block to enhance helpful information. Also, we introduce a new dataset for the MER task that consists of 10,438 samples with valence/arousal labels. The results show that using chorus parts of music can improve the deep models' performance. Also, experiments show our proposed method achieves 15% and 40% relative improvement of valence and arousal, respectively, on the R2 score compared to the state-of-the-art and baseline models.