论文标题
一种新型的多模式音乐流派分类器，使用分层关注和卷积神经网络
A Novel Multimodal Music Genre Classifier using Hierarchical Attention and Convolutional Neural Network
论文作者
论文摘要
音乐流派分类是有关当前音乐信息检索（MIR）研究的趋势主题之一。由于类型的依赖性不仅限于音频配置文件，因此我们还将提供的文本内容作为相应歌曲的歌词。我们为频谱图实施了基于CNN的功能提取器，以便合并声学特征和基于分层注意力网络的基于层次的歌词提取器。然后，我们继续根据所得的融合功能向量对音乐曲目进行分类。
Music genre classification is one of the trending topics in regards to the current Music Information Retrieval (MIR) Research. Since, the dependency of genre is not only limited to the audio profile, we also make use of textual content provided as lyrics of the corresponding song. We implemented a CNN based feature extractor for spectrograms in order to incorporate the acoustic features and a Hierarchical Attention Network based feature extractor for lyrics. We then go on to classify the music track based upon the resulting fused feature vector.
