用注意力机制捕捉音乐时序关键片段，提升流派分类准确率。
Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification
- 通过CNN与多头注意力处理频谱序列，聚焦重要时间片段。
- 有效识别流派特异性特征，提升分类性能。
- 适合音乐推荐与人感匹配的研究者参考。
音乐流派分类是音乐推荐系统、生成算法和文化分析的核心组成部分。本文提出一种基于注意力机制的时序特征建模方法，通过卷积神经网络（CNN）和多头注意力层处理频谱序列，捕捉每首音乐中最关键的时序片段，构建用于流派识别的独特“签名”。该方法不仅提升了分类准确率，还揭示了流派特有特征，可直观映射到听觉感知。研究结果为个性化音乐推荐系统提供新思路，揭示跨流派的共性与差异，与人类音乐直觉高度契合，弥合了技术分类与人类体验之间的鸿沟。
原文摘要 · Abstract (English)
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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