Acoustic scene classification using asynchronous multichannel observations with different lengths

To utilize asynchronous multichannel recordings with different start and end time of recordings for acoustic scene analysis, we propose a combination method for estimating unrecorded durations and extracting spatial features. Focusing on the fact that amplitude information is relatively robust to the estimation error of the unrecorded durations and the synchronization mismatch of multichannel recordings, the proposed method combines a multiple imputation and spatial cepstrum, both of which are based on the amplitude information. The proposed method allows us to estimate unrecorded durations in multichannel observations and analyze acoustic scenes using spatial information extracted from whole multichannel observations, thus achieving more accurate scene analysis. An evaluation experiment indicated that the proposed method is valid for acoustic scene classification with asynchronous multichannel recordings including different length.