  Sounds recorded with smartphones or IoT devices often have partially
unreliable observations caused by clipping, wind noise, and completely missing
parts due to microphone failure and packet loss in data transmission over the
network. 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 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.
