Statistical Analysis of Musical Features for Emotional Semantic Differentiation in Human and AI Databases

• Type: Article in International Conference Proceedings Book
• Date: 2024-07-06

Authors

Francisco Braga (Author) Other; Jorge Forero (Author) FEUP; Gilberto Bernardes (Author) FEUP

Conference proceedings International

Title: Proceedings of the 21st Sound and Music Computing Conference

Pages: 241-246

Other information

Abstract (EN): Understanding the structural features of perceived musical emotions is crucial for various applications, including content generation and mood-driven playlists. This study performs a comparative statistical analysis to examine the association of a set of musical features with emotions, described using adjectives. The analysis uses two datasets containing rock and pop musical fragments, categorized as human-generated and AI-generated. Focusing on four emotional adjectives (happy, sad, angry, tender-gentle) representing each valence-arousal plane’s quadrant, we analyzed semantic differential meanings reported as symmetric pairs for all possible combinations of quadrants through diagonals, vertical, and horizontal axes. The results obtained were discussed based on Living- stone’s circular representation of emotional features in mu- sic. Our findings demonstrate that the human and AI-generated datasets could be considered equivalent for diagonal symmetries, while horizontal and vertical symmetries show discrepancies. Furthermore, we assessed significant separability for both happy-sad and angry-tender pairs in the human dataset. In contrast, the AI-generated music exhibits a strong differentiation mainly in the angry-gentle pair.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6