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.

中文翻译：

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使用具有部分丢失通道的多通道观察的声场分类

用智能手机或IoT设备记录的声音通常具有部分不可靠的观测结果，这是由于麦克风故障和网络数据传输中的数据包丢失所导致的削波，风噪声以及完全丢失的零件所致。在本文中，我们调查了部分丢失的通道对使用多通道音频记录的声学场景分类性能的影响，特别是对于分布式麦克风阵列。缺少观测值不仅会导致声源的时频和空间信息丢失，还会导致训练后的模型与评估数据不匹配。因此，我们详细研究了丢失的通道如何影响声学场景分类的性能。