  End-to-end learning models using raw waveforms as input have shown superior
performances in many audio recognition tasks. However, most model architectures
are based on convolutional neural networks (CNN) which were mainly developed
for visual recognition tasks. In this paper, we propose an extension of
squeeze-and-excitation networks (SENets) which adds temporal feedback control
from the top-layer features to channel-wise feature activations in lower layers
using a recurrent module. This is analogous to the adaptive gain control
mechanism of outer hair-cell in the human auditory system. We apply the
proposed model to speech command recognition and show that it slightly
outperforms the SENets and other CNN-based models. We also investigate the
details of the performance improvement by conducting failure analysis and
visualizing the channel-wise feature scaling induced by the temporal feedback.
