  We propose a two-stream convolutional network for audio recognition, that
operates on time-frequency spectrogram inputs. Following similar success in
visual recognition, we learn Slow-Fast auditory streams with separable
convolutions and multi-level lateral connections. The Slow pathway has high
channel capacity while the Fast pathway operates at a fine-grained temporal
resolution. We showcase the importance of our two-stream proposal on two
diverse datasets: VGG-Sound and EPIC-KITCHENS-100, and achieve state-of-the-art
results on both.
