Multi-scale semantic feature fusion and data augmentation for acoustic scene classification

Abstract

This paper investigates a multi-scale semantic feature fusion and data augmentation approach for deep convolutional neural network (CNN) based acoustic scene classification. To ensemble the multi-scale semantic information of CNN and improve the performance of acoustic scene classification, a multi-scale feature fusion framework, which consists of a simplified Xception backbone and a semantic feature fusion strategy, is presented. A novel label smoothing mixup data augmentation method, which is a generalization of mixup and label smoothing, is proposed to alleviate the over-confident problem of network training. A spatial-mixup technique is presented to generate meaningful mixup virtual data for acoustic scene classification. Extensive experiments on synthetic data and real acoustic scene classification dataset demonstrate that both multi-scale semantic feature fusion and label smoothing spatial-mixup data augmentation are effective for improving the acoustic scene classification performance of a deep neural network.