  In this paper, we compare the performance of using binaural audio features in
place of single-channel features for sound event detection. Three different
binaural features are studied and evaluated on the publicly available TUT Sound
Events 2017 dataset of length 70 minutes. Sound event detection is performed
separately with single-channel and binaural features using stacked
convolutional and recurrent neural network and the evaluation is reported using
standard metrics of error rate and F-score. The studied binaural features are
seen to consistently perform equal to or better than the single-channel
features with respect to error rate metric.
