Feature Alignment for Robust Acoustic Scene Classification Across Devices

Abstract

This letter presents a feature alignment method for domain adaptive Acoustic Scene Classification (ASC) across recording devices. First, we design a two-stream network, in which each stream processes two features, i.e., Log-Mel spectrogram and delta-deltas, using two sub-networks. Second, we investigate different loss functions for feature alignment between the feature maps obtained by the source and target domains. Last, we present an alternate training strategy to deal with the data imbalance problem between paired and unpaired samples. The experimental results obtained on the DCASE benchmarks demonstrate the effectiveness and superiority of the proposed method. The source code of the proposed method is available at https://github.com/Jingqiao-Zhao/FAASC.