Violin Music Emotion Recognition with Fusion of CNN–BiGRU and Attention Mechanism

Abstract

Music emotion recognition has garnered significant interest in recent years, as the emotions expressed through music can profoundly enhance our understanding of its deeper meanings. The violin, with its distinctive emotional expressiveness, has become a focal point in this field of research. To address the scarcity of specialized data, we developed a dataset specifically for violin music emotion recognition named VioMusic. This dataset offers a precise and comprehensive platform for the analysis of emotional expressions in violin music, featuring specialized samples and evaluations. Moreover, we implemented the CNN–BiGRU–Attention (CBA) model to establish a baseline system for music emotion recognition. Our experimental findings show that the CBA model effectively captures the emotional nuances in violin music, achieving mean absolute errors (MAE) of 0.124 and 0.129. The VioMusic dataset proves to be highly practical for advancing the study of emotion recognition in violin music, providing valuable insights and a robust framework for future research.

Keywords

• music dataset
• music emotion recognition
• deep learning