基于频谱空间域特征注意的音乐流派分类算法

为了提升深度卷积神经网络对音乐频谱流派特征的提取效果，提出一种基于频谱空间域特征注意的音乐流派分类算法模型DCNN-SSA。DCNN-SSA模型通过对不同音乐梅尔谱图的流派特征在空间域上进行有效标注，并且改变网络结构，从而在提升特征提取效果的同时确保模型的有效性，进而提升音乐流派分类的准确率。首先，将原始音频信号进行梅尔滤波，以模拟人耳的滤波操作对音乐的音强及节奏变化进行有效过滤，所生成的梅尔谱图进行切割后输入网络；然后，通过深化网络层数、改变卷积结构及增加空间注意力机制对模型在流派特征提取上进行增强；最后，通过在数据集上进行多批次的训练与验证来有效提取并学习音乐流派特征，从而得到可以对音乐流派进行有效分类的模型。在GTZAN数据集上的实验结果表明，基于空间注意的音乐流派分类算法与其他深度学习模型相比，在音乐流派分类准确率和模型收敛效果上有所提高，准确率提升了5.36个百分点～10.44个百分点。

Music genre classification algorithm based on attention spectral-spatial feature

In order to improve the extraction effect of the deep convolutional neural network on music spectrum genre features， a music genre classification algorithm model based on attention spectral-spatial feature， namely DCNN-SSA （Deep Convolutional Neural Network Spectral Spatial Attention）， was proposed. In DCNN-SSA model， the genre features of different music Mel spectrograms were effectively annotated in the spatial domain， and the network structure was changed to improve the feature extraction effect while ensuring the effectiveness of the model， thereby improving the accuracy of music genre classification. Firstly， the original audio signals were Mel-filtered to effectively filter the sound intensity and rhythm change of the music by simulating the filtering operation of the human ear， and the generated Mel spectrograms were cut and input into the network. Then， the model was enhanced in genre feature extraction by deepening the number of network layers， changing the convolution structure and adding spatial attention mechanism. Finally， through multiple batches of training and verification on the dataset， the features of music genres were extracted and learned effectively， and a model that can effectively classify music genres was obtained. Experimental results on GTZAN dataset show that compared with other deep learning models， the music genre classification algorithm based on spatial attention increases the music genre classification accuracy by 5.36 percentage points to 10.44 percentage points and improves model convergence effect.