An AI based approach to classifying genre and emotion of music using spectrograms
DOI:
https://doi.org/10.58445/rars.3107Abstract
Music classification has become an essential feature of modern technology, from recommendation systems and mood-based playlists to therapeutic interventions. Beyond genre classification, recent work emphasizes the importance of recognizing the emotional tone of music. This study investigates whether spectrogram-based image classification can be applied using simple, accessible AI tools. Specifically, I ask: (1) Can spectrograms serve as reliable features for both genre and emotion classification? and (2) How accurate can a lightweight AI tool be in this task? Using the Emotify dataset, I trained two models—one for genre and one for emotion. Results showed moderate success in genre classification (57% accuracy vs 25% chance levels) but poor performance in emotion classification (21% accuracy vs 11% chance levels). Findings suggest that spectrograms capture some, but not all aspects of genre and emotion related differences, that can be reliably detected using simple AI tools.
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