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

arXiv: https://arxiv.org/abs/2411.14474 · HTML: https://mlsanity.com/paper/2411.14474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification](https://mlsanity.com/paper/1802.09697/) (2018)

## Abstract (arXiv)

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.