Multi-scale Fusion and Channel Weighted CNN for Acoustic Scene Classification

Abstract

Ensemble semantic features are useful for acoustic scene classification. In this paper, we proposed a multi-scale fusion and channel weighted CNN framework. The framework consists of two stages: the multi-scale feature fusion and channel weighting stages. The multi-scale feature fusion stage extracts hierarchy semantic feature maps using a CNN with simplified Xception architecture and then integrates multi-scale semantic features through a top-down pathway. The channel weighting stage squeezes feature maps into a channel descriptor and then transforms it into a set of channel weighting factors to reinforce the importance of each channel for acoustic scene classification. Experimental results on DCASE2018 acoustic scene classification subtask A and subtask B demonstrate the performances of the proposed framework.