Effective Music Emotion Recognition by Segment-based Progressive Learning

Music has always been a popular media because it can relax our pressure of life. However, the music appealing to an individual could shift under his/her different emotions. For example, the preferred music in a sad mode is very possibly different from that in a happy manner. Therefore, effectively representing the human sense hidden in music can link the user emotion to music. To aim at this issue, Music Retrieval Information (MIR) were proposed for recognizing musical emotion. In the past, although some studies have been made on music emotion recognition, their effectiveness is not satisfactory. A potential reason is that the audio features extracted are not robust enough to discriminate the diversity between music and emotion. Hence, in this paper, we propose an effective music recognition method, which fuses Deep Learning (DL) and Support Vector Machine (SVM). The major difference between the proposed method and traditional audio-based studies is that the proposed method aggregates the partial recognition results of music to achieve the better recognition precision. The experimental results on a real dataset of CAL500 show that the proposed method performs better than some other audio-based music emotion labeling methods.