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arXiv · 2411.14474

Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification

Aditya Sridhar
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Abstract

Music genre classification is a critical component of music recommendation systems, generation algorithms, and cultural analytics. In this work, we present an innovative model for classifying music genres using attention-based temporal signature modeling. By processing spectrogram sequences through Convolutional Neural Networks (CNNs) and multi-head attention layers, our approach captures the most temporally significant moments within each piece, crafting a unique "signature" for genre identification. This temporal focus not only enhances classification accuracy but also reveals insights into genre-specific characteristics that can be intuitively mapped to listener perceptions. Our findings offer potential applications in personalized music recommendation systems by highlighting cross-genre similarities and distinctiveness, aligning closely with human musical intuition. This work bridges the gap between technical classification tasks and the nuanced, human experience of genre.

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BibTeXRIS

Aditya Sridhar. 2024-11-18. Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification. https://arxiv.org/abs/2411.14474

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