EMOTION RECOGNITION IN AI-GENERATED MUSIC
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6763Keywords:
Emotion Recognition, AI-Generated Music, Affective Computing, Music Cognition, Valence–Arousal, Human–AI Interaction, Music TherapyAbstract [English]
The paper under investigation explores the problem of emotion recognition in AI-generated music by applying the concepts of cognitive psychology, affective neuroscience, and computational creativity. It presents a valence-arousal modeling, deep neural architecture and listener-based feedback loop approach to the emotional interpretation, generation, and critical analysis of emotional signals in music through the use of AI. The system uses hybrid CNN -RNN and Transformer models to derive the temporal-spectral features and project them onto the affective dimensions including valence, arousal, and tension. Managerial-level evaluation A multi-stage test that integrates perceptual ratings, physiological sensing and behavioral analysis proves the emotional fidelity of the system, with the system able to yield high classification accuracy of 92.4% and a r correlation coefficient of r = 0.89 between predicted and perceived emotional levels in humans. Findings are valid that although AI is efficient in reproducing the syntax of emotion, its perception is representational and not experiential. Ethical issues related to authenticity, authorship and privacy are considered with a focus on transparent and culture-inclusive model design. Altogether, the study contributes to computational affective musicology which places AI as a creative partner that recreates and enhances emotional expression, but not eliminates it.
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Copyright (c) 2025 Deepak Prasad, Prateek Garg, Tanveer Ahmad Wani, Sangeet Saroha, Dr. Varalakshmi S, Simran Kalra, Avinash Somatkar
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