  In this paper, we propose a stacked convolutional and recurrent neural
network (CRNN) with a 3D convolutional neural network (CNN) in the first layer
for the multichannel sound event detection (SED) task. The 3D CNN enables the
network to simultaneously learn the inter- and intra-channel features from the
input multichannel audio. In order to evaluate the proposed method,
multichannel audio datasets with different number of overlapping sound sources
are synthesized. Each of this dataset has a four-channel first-order Ambisonic,
binaural, and single-channel versions, on which the performance of SED using
the proposed method are compared to study the potential of SED using
multichannel audio. A similar study is also done with the binaural and
single-channel versions of the real-life recording TUT-SED 2017 development
dataset. The proposed method learns to recognize overlapping sound events from
multichannel features faster and performs better SED with a fewer number of
training epochs. The results show that on using multichannel Ambisonic audio in
place of single-channel audio we improve the overall F-score by 7.5%, overall
error rate by 10% and recognize 15.6% more sound events in time frames with
four overlapping sound sources.
