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

• URL: http://arxiv.org/abs/2411.14474v1
• Date: Mon, 18 Nov 2024 21:57:03 GMT
• Title: Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification
• Authors: Aditya Sridhar,
• Abstract summary: We present an innovative model for classifying music genres using attention-based temporal signature modeling. Our approach captures the most temporally significant moments within each piece, crafting a unique "signature" for genre identification. This work bridges the gap between technical classification tasks and the nuanced, human experience of genre.
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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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