Evangelos Kazakos1, Arsha Nagrani2, Andrew Zisserman2 and Dima Damen1

1University of Bristol, Dept. of Computer Science, 2University of Oxford, VGG

Overview

Outstanding paper award at ICASSP 2021!

Abstract

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.

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Bibtex


@INPROCEEDINGS{Kazakos2021SlowFastAuditory,
  author={Kazakos, Evangelos and Nagrani, Arsha and Zisserman, Andrew and Damen, Dima},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Slow-Fast Auditory Streams for Audio Recognition}, 
  year={2021},
  pages={855-859},
  doi={10.1109/ICASSP39728.2021.9413376}}

Acknowledgements

Kazakos is supported by EPSRC DTP, Damen by EPSRC Fellowship UMPIRE (EP/T004991/1) and Nagrani by Google PhD fellowship. Research is also supported by Seebibyte (EP/M013774/1).