Conformer-based Self-supervised Learning for Non-speech Audio Tasks

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)


Representation learning from unlabeled data has been of major interest in artificial intelligence research. While self-supervised speech representation learning has been popular in the speech research community, very few works have comprehensively analyzed audio representation learning for non-speech audio tasks. In this paper, we propose a self-supervised audio representation learning method and apply it to a variety of downstream non-speech audio tasks. We combine the well-known wav2vec 2.0 framework, which has shown success in self-supervised learning for speech tasks, with parameter-efficient conformer architectures. Our self-supervised pre-training can reduce the need for labeled data by two-thirds. On the AudioSet benchmark, we achieve a mean average precision (mAP) score of 0.415, which is a new state-of-the-art on this dataset through audio-only self-supervised learning. Our fine-tuned conformers also surpass or match the performance of previous systems pre-trained in a supervised way on several downstream tasks. We further discuss the important design considerations for both pre-training and fine-tuning.

Latest Publications

A Practical Stereo Depth System for Smart Glasses

Jialiang Wang, Daniel Scharstein, Akash Bapat, Kevin Blackburn-Matzen Matthew Yu, Jonathan Lehman, Suhib Alsisan, Yanghan Wang, Sam Tsai, Jan-Michael Frahm, Zijian He, Peter Vajda, Michael Cohen, Matt Uyttendaele

CVPR - 2023

Presto: A Decade of SQL Analytics at Meta

Yutian James Sun, Tim Meehan, Rebecca Schlussel, Wenlei Xie, Masha Basmanova, Orri Erling, Andrii Rosa, Shixuan Fan, Rongrong Zhong, Arun Thirupathi, Nikhil Collooru, Ke Wang, Sameer Agarwal, Arjun Gupta, Dionysios Logothetis, Kostas Xirogiannopoulos, Bin Fan, Amit Dutta, Varun Gajjala, Rohit Jain, Ajay Palakuzhy, Prithvi Pandian, Sergey Pershin, Abhisek Saikia, Pranjal Shankhdhar, Neerad Somanchi, Swapnil Tailor, Jialiang Tan, Sreeni Viswanadha, Zac Wen, Deepak Majeti, Aditi Pandit, Biswapesh Chattopadhyay

SIGMOD - 2023