An Adversarial Feature Distillation Method for Audio Classification

Abstract

The audio classification task aims to discriminate between different audio signal types. In this task, deep neural networks have achieved better performance than the traditional shallow architecture-based machine-learning method. However, deep neural networks often require huge computational and storage requirements that hinder the deployment in embedded devices. In this paper, we proposed a distillation method which transfers knowledge from well-trained networks to a small network, and the method can compress model size while improving audio classification precision. The contributions of the proposed method are two folds: a multi-level feature distillation method was proposed and an adversarial learning strategy was employed to improve the knowledge transfer. The extensive experiments are conducted on three audio classification tasks, audio scene classification, general audio tagging, and speech command recognition. The experimental results demonstrate that: the small network can provide better performance while achieves the calculated amount of floating-point operations per second (FLOPS) compression ratio of 76:1 and parameters compression ratio of 3:1.

Keywords

• Convolutional neural networks
• audio tagging
• knowledge distillation
• model compression