Sound Classification Based on Multihead Attention and Support Vector Machine

Sound classification is a broad area of research that has gained much attention in recent years. The sound classification systems based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have undergone significant enhancements in the recognition capability of models. However, their computational complexity and inadequate exploration of global dependencies for long sequences restrict improvements in their classification results. In this paper, we show that there are still opportunities to improve the performance of sound classification by substituting the recurrent architecture with the parallel processing structure in the feature extraction. In light of the small-scale and high-dimension sound datasets, we propose the use of the multihead attention and support vector machine (SVM) for sound taxonomy. The multihead attention is taken as the feature extractor to obtain salient features, and SVM is taken as the classifier to recognize all categories. Extensive experiments are conducted across three acoustically characterized public datasets, UrbanSound8K, GTZAN, and IEMOCAP, by using two commonly used audio spectrograms as inputs, respectively, and we fully evaluate the impact of parameters and feature types on classification accuracy. Our results suggest that the proposed model can reach comparable performance with existing methods and reveal its strong generalization ability of sound taxonomy.