  In this study, we introduce a convolutional time-frequency-channel "Squeeze
and Excitation" (tfc-SE) module to explicitly model inter-dependencies between
the time-frequency domain and multiple channels. The tfc-SE module consists of
two parts: tf-SE block and c-SE block which are designed to provide attention
on time-frequency and channel domain, respectively, for adaptively
recalibrating the input feature map. The proposed tfc-SE module, together with
a popular Convolutional Recurrent Neural Network (CRNN) model, are evaluated on
a multi-channel sound event detection task with overlapping audio sources: the
training and test data are synthesized TUT Sound Events 2018 datasets, recorded
with microphone arrays. We show that the tfc-SE module can be incorporated into
the CRNN model at a small additional computational cost and bring significant
improvements on sound event detection accuracy. We also perform detailed
ablation studies by analyzing various factors that may influence the
performance of the SE blocks. We show that with the best tfc-SE block, error
rate (ER) decreases from 0.2538 to 0.2026, relative 20.17\% reduction of ER,
and 5.72\% improvement of F1 score. The results indicate that the learned
acoustic embeddings with the tfc-SE module efficiently strengthen
time-frequency and channel-wise feature representations to improve the
discriminative performance.
