Acoustic Scene Classification Using Multichannel Observation with Partially Missing Channels

Sounds recorded with smartphones or IoT devices often have completely missing parts due to microphone failure and packet loss in data transmission over the network, and partially unreliable observations caused by clipping, wind noise. In this paper, we investigate the impact of the partially missing channels on the performance of acoustic scene classification using multichannel audio recordings, especially for a distributed microphone array. Missing observations cause not only losses of time-frequency and spatial information on sound sources but also a mismatch between a trained model and evaluation data. We thus investigate how a missing channel caused by the microphone failure and packet loss affects the performance of acoustic scene classification in detail. We also propose simple data augmentation methods for scene classification using multichannel observations with partially missing channels and evaluate the scene classification performance using the data augmentation methods.