A Novel Music Emotion Recognition Model for Scratch-generated Music

• Published in: 2020 International Wireless Communications and Mobile Computing (IWCMC) pp. 1794 - 1799
• Main Authors: Gao, Zijing, Qiu, Lichen, Qi, Peng, Sun, Yan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2020
• Subjects: CRNN; emotion; Emotion recognition; Feature extraction; Instruments; Multiple signal classification; Music; recognition; Scratch-generated; Spectrogram
• ISSN: 2376-6506
• DOI: 10.1109/IWCMC48107.2020.9148471

In recent years, Scratch has been a popular programming platform for young children. To help children express emotions for projects, Scratch provides children with a music module to create desirable background music. However, in Scratch, there is not a tool helping recognize the emotion of music. Besides, as Scratch-generated music differs from regular music, existing music emotion recognition models perform poor in Scratch-generated music. To overcome it, in this paper, we propose a novel music emotion recognition model for Scratch-generated music. First, we build a Scratch-generated dataset by the main melody extraction algorithm. Then, for each music, we extract their underlying features and input them to the CNN module. After that, the features learned by CNN are input to RNN to get the final classification results. In our model, the CNN module can learn the important features of music while RNN can learn the sequential features. The experimental results show that the proposed model performs better than traditional music emotion recognition models.