Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification
This work addresses music genre classification for applications in recommendation systems and cultural analytics, representing an incremental improvement with a hybrid method.
The authors tackled music genre classification by developing an attention-based temporal signature model using CNNs and multi-head attention layers to capture significant moments in spectrogram sequences, resulting in enhanced classification accuracy and insights into genre-specific characteristics.
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.