通过子成分注意力与集成学习提升音乐流派分类准确率
Music Genre Classification: Ensemble Learning with Subcomponents-level Attention
- 分拆音乐成分独立建模，捕捉各部分特征
- 在GTZAN数据集上优于现有最先进方法
- 适合音乐信息检索与深度学习研究者
音乐流派分类是音乐信息检索（MIR）和数字信号处理领域的热门课题。深度学习已成为各类方法中表现最优的分类手段。本文提出一种新方法，将集成学习与子成分级注意力相结合，旨在提升音乐流派识别的准确性。核心创新在于对音乐作品的子成分进行单独分类，使模型能够捕获各子成分的独特特征。通过将这些独立分类结果整合，最终决定音乐的流派。该方法在GTZAN数据集上的训练与测试中，相较其他最先进的技术展现出显著更高的准确率。
原文摘要 · Abstract (English)
Music Genre Classification is one of the most popular topics in the fields of Music Information Retrieval (MIR) and digital signal processing. Deep Learning has emerged as the top performer for classifying music genres among various methods. The letter introduces a novel approach by combining ensemble learning with attention to sub-components, aiming to enhance the accuracy of identifying music genres. The core innovation of our work is the proposal to classify the subcomponents of the music pieces separately, allowing our model to capture distinct characteristics from those sub components. By applying ensemble learning techniques to these individual classifications, we make the final classification decision on the genre of the music. The proposed method has superior advantages in terms of accuracy compared to the other state-of-the-art techniques trained and tested on the GTZAN dataset.
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