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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 10: AAAI-22 Technical Tracks 10

SSAST: Self-Supervised Audio Spectrogram Transformer

February 1, 2023

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Authors

Yuan Gong

MIT Computer Science and Artificial Intelligence Laboratory


Cheng-I Lai

MIT Computer Science and Artificial Intelligence Laboratory


Yu-An Chung

MIT Computer Science and Artificial Intelligence Laboratory


James Glass

MIT Computer Science and Artificial Intelligence Laboratory


DOI:

10.1609/aaai.v36i10.21315


Abstract:

Recently, neural networks based purely on self-attention, such as the Vision Transformer (ViT), have been shown to outperform deep learning models constructed with convolutional neural networks (CNNs) on various vision tasks, thus extending the success of Transformers, which were originally developed for language processing, to the vision domain. A recent study showed that a similar methodology can also be applied to the audio domain. Specifically, the Audio Spectrogram Transformer (AST) achieves state-of-the-art results on various audio classification benchmarks. However, pure Transformer models tend to require more training data compared to CNNs, and the success of the AST relies on supervised pretraining that requires a large amount of labeled data and a complex training pipeline, thus limiting the practical usage of AST. This paper focuses on audio and speech classification, and aims to reduce the need for large amounts of labeled data for the AST by leveraging self-supervised learning using unlabeled data. Specifically, we propose to pretrain the AST model with joint discriminative and generative masked spectrogram patch modeling (MSPM) using unlabeled audio from AudioSet and Librispeech. We evaluate our pretrained models on both audio and speech classification tasks including audio event classification, keyword spotting, emotion recognition, and speaker identification. The proposed self-supervised framework significantly boosts AST performance on all tasks, with an average improvement of 60.9%, leading to similar or even better results than a supervised pretrained AST. To the best of our knowledge, it is the first patch-based self-supervised learning framework in the audio and speech domain, and also the first self-supervised learning framework for AST.

Topics: AAAI

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HOW TO CITE:

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass SSAST: Self-Supervised Audio Spectrogram Transformer Proceedings of the AAAI Conference on Artificial Intelligence (2022) 10699-10709.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass SSAST: Self-Supervised Audio Spectrogram Transformer AAAI 2022, 10699-10709.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass (2022). SSAST: Self-Supervised Audio Spectrogram Transformer. Proceedings of the AAAI Conference on Artificial Intelligence, 10699-10709.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass. SSAST: Self-Supervised Audio Spectrogram Transformer. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.10699-10709.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass. 2022. SSAST: Self-Supervised Audio Spectrogram Transformer. "Proceedings of the AAAI Conference on Artificial Intelligence". 10699-10709.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass. (2022) "SSAST: Self-Supervised Audio Spectrogram Transformer", Proceedings of the AAAI Conference on Artificial Intelligence, p.10699-10709

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass, "SSAST: Self-Supervised Audio Spectrogram Transformer", AAAI, p.10699-10709, 2022.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass. "SSAST: Self-Supervised Audio Spectrogram Transformer". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.10699-10709.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass. "SSAST: Self-Supervised Audio Spectrogram Transformer". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 10699-10709.

Yuan Gong||Cheng-I Lai||Yu-An Chung||James Glass. SSAST: Self-Supervised Audio Spectrogram Transformer. AAAI[Internet]. 2022[cited 2023]; 10699-10709.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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