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Transformers have rapidly become the preferred choice for audio classification, surpassing methods based on CNNs.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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AudioSet: An ontology and human-labeled dataset for audio events
Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Speech Commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
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Self-supervised multimodal versatile networks
Jean-Baptiste Alayrac, Adria Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, and Andrew Zisserman · 2020
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VGGSound: A large-scale audio-visual dataset
Honglie Chen, Weidi Xie, Andrea Vedaldi, and Andrew Zisserman · 2020
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Panns: Large-scale pretrained audio neural networks for audio pattern recognition
Qiuqiang Kong, Yin Cao, Turab Iqbal, Yuxuan Wang, Wenwu Wang, and Mark D Plumbley · 2020
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Learning representations from audio-visual spatial alignment
Pedro Morgado, Yi Li, and Nuno Nvasconcelos · 2020
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VoxCeleb: Large-scale speaker verification in the wild
Arsha Nagrani, Joon Son Chung, Weidi Xie, and Andrew Zisserman · 2020
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Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text
Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong · 2021
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, et al · 2021
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AST: audio spectrogram transformer
Yuan Gong, Yu-An Chung, and James Glass · 2021
Cited alongside, same era.
Attention bottlenecks for multimodal fusion
Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen, Cordelia Schmid, and Chen Sun · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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MAE-AST: masked autoencoding audio spectrogram transformer
Alan Baade, Puyuan Peng, and David Harwath · 2022
Cited alongside, same era.
HTS-AT: a hierarchical token-semantic audio transformer for sound classification and detection
Ke Chen, Xingjian Du, Bilei Zhu, Zejun Ma, Taylor Berg-Kirkpatrick, and Shlomo Dubnov · 2022
Cited alongside, same era.
Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100
Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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EPIC-SOUNDS: A Large-Scale Dataset of Actions that Sound
Jaesung Huh, Jacob Chalk, Evangelos Kazakos, Dima Damen, and Andrew Zisserman · 2023
Later among the works it cites.
Speechclip: Integrating speech with pre-trained vision and language model
Yi-Jen Shih, Hsuan-Fu Wang, Heng-Jui Chang, Layne Berry, Hung-yi Lee, and David Harwath · 2023
Later among the works it cites.
Simplified state space layers for sequence modeling
Jimmy TH Smith, Andrew Warrington, and Scott W Linderman · 2023
Later among the works it cites.
Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation
Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov · 2023
Later among the works it cites.
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2022
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Masked autoencoders that listen
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Wav2clip: Learning robust audio representations from clip
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Hungry hungry hippos: Towards language modeling with state space models
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Uavm: Towards unifying audio and visual models
Yuan Gong, Alexander H Liu, Andrew Rouditchenko, and James Glass
Cited in the paper.
Contrastive audio-visual masked autoencoder
Yuan Gong, Andrew Rouditchenko, Alexander H Liu, David Harwath, Leonid Karlinsky, Hilde Kuehne, and James Glass
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