Advancing Music Emotion Recognition: A Transformer Encoder-Based Approach

Music Emotion Recognition (MER) involves identifying the emotional content conveyed by music. This field is becoming increasingly significant due to its broad range of applications, including music recommendation systems, mood-based playlists, and therapeutic tools. This paper presents a novel MER model designed for song-level analysis, leveraging the Transformer Encoder architecture. The model incorporates various embedding techniques to capture both local and global contexts within musical data, thereby improving the extraction of crucial features for emotion recognition. Additionally, a Self-Attention Pooling Layer is used to effectively integrate and interpret complex musical features. Experiments using the DEAM dataset reveal that this model excels in emotion identification, surpassing existing approaches and offering promising directions for future research in the field of MER.