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This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms.
D. Griffin and J. Lim, “Signal estimation from modified short-time fourier transform,” IEEE Transactions on Acoustics, Speech, and Signal Processing , vol. 32, no. 2, pp. 236–243, 1984
1984
Earlier work this paper cites.
Y. Suzuki and H. Takeshima, “Equal-loudness-level contours for pure tones,” The Journal of the Acoustical Society of America , vol. 116, no. 2, pp. 918–933, 2004
2004
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 20-26 June 2005, San Diego, CA, USA . IEEE Computer Society, 2005, pp. 886–893
2005
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008 , ser. ACM International Conference Proceeding Series, vol. 307. ACM, 2008, pp. 1096–1103
2008
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J. Mach. Learn. Res. , vol. 11, pp. 3371–3408, 2010
2010
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz et al. , “The kaldi speech recognition toolkit,” in IEEE 2011 workshop on automatic speech recognition and understanding , no. CONF. IEEE Signal Processing Society, 2011
2011
Earlier work this paper cites.
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
K. J. Piczak, “ESC: Dataset for Environmental Sound Classification,” in Proceedings of the 23rd Annual ACM Conference on Multimedia . ACM Press, 2015, pp. 1015–1018
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 770–778, 2015
2015
Earlier work this paper cites.
B. Liu, J. Tao, Z. Wen, Y. Li, and D. Bukhari, “A novel method of artificial bandwidth extension using deep architecture,” in INTERSPEECH 2015, 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, September 6-10, 2015 . ISCA, 2015, pp. 2598–2602
2015
Earlier work this paper cites.
C. Feichtenhofer, A. Pinz, and R. P. Wildes, “Spatiotemporal residual networks for video action recognition,” in NIPS , 2016
2016
Earlier work this paper cites.
D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, 2016, pp. 2536–2544
2016
Earlier work this paper cites.
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, “Deep networks with stochastic depth,” in Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV , ser. Lecture Notes in Computer Science, vol. 9908. Springer, 2016, pp. 646–661
2016
Earlier work this paper cites.
B. Lee and J. Chang, “Packet loss concealment based on deep neural networks for digital speech transmission,” IEEE ACM Trans. Audio Speech Lang. Process. , vol. 24, no. 2, pp. 378–387, 2016
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. USA: Curran Associates Inc., 2017, pp. 6000–6010
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio set: An ontology and human-labeled dataset for audio events,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2017, pp. 776–780
2017
Earlier work this paper cites.
J. Carreira and A. Zisserman, “Quo vadis, action recognition? A new model and the kinetics dataset,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 . IEEE Computer Society, 2017, pp. 4724–4733
2017
Earlier work this paper cites.
S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold, M. Slaney, R. Weiss, and K. Wilson, “Cnn architectures for large-scale audio classification,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2017
2017
Earlier work this paper cites.
——, “SGDR: stochastic gradient descent with warm restarts,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net, 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
R. Arandjelovic and A. Zisserman, “Objects that sound,” in Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part I , ser. Lecture Notes in Computer Science, vol. 11205. Springer, 2018, pp. 451–466
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Warden, “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition,” ArXiv e-prints , Apr. 2018
2018
Earlier work this paper cites.
H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net, 2018
2018
Cited alongside, same era.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, 2019, pp. 4171–4186
2019
Cited alongside, same era.
2019
Cited alongside, same era.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in International Conference on Machine Learning . PMLR, 2021, pp. 10 347–10 357
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021 . IEEE, 2021, pp. 9992–10 002
2021
Later among the works it cites.
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S. Schneider, A. Baevski, R. Collobert, and M. Auli, “wav2vec: Unsupervised pre-training for speech recognition,” in Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019 . ISCA, 2019, pp. 3465–3469
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Yun, D. Han, S. Chun, S. J. Oh, Y. Yoo, and J. Choe, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019 . IEEE, 2019, pp. 6022–6031
2019
Cited alongside, same era.
M. Tan and Q. V. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA , ser. Proceedings of Machine Learning Research, vol. 97. PMLR, 2019, pp. 6105–6114
2019
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019
2019
Cited alongside, same era.
R. Müller, S. Kornblith, and G. E. Hinton, “When does label smoothing help?” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , 2019, pp. 4696–4705
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. Marafioti, N. Perraudin, N. Holighaus, and P. Majdak, “A context encoder for audio inpainting,” IEEE ACM Trans. Audio Speech Lang. Process. , vol. 27, no. 12, pp. 2362–2372, 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 139. PMLR, 2021, pp. 8821–8831
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Gong, Y. Chung, and J. Glass, “Psla: Improving audio tagging with pretraining, sampling, labeling, and aggregation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2021
2021
Later among the works it cites.
W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 3451–3460, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Patrick, P. Huang, I. Misra, F. Metze, A. Vedaldi, Y. M. Asano, and J. F. Henriques, “Space-time crop & attend: Improving cross-modal video representation learning,” in 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021 . IEEE, 2021, pp. 10 540–10 552
2021
Later among the works it cites.
S. Hershey, D. P. Ellis, E. Fonseca, A. Jansen, C. Liu, R. C. Moore, and M. Plakal, “The benefit of temporally-strong labels in audio event classification,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 366–370
2021
Later among the works it cites.
S. wen Yang, P.-H. Chi, Y.-S. Chuang, C.-I. J. Lai, K. Lakhotia, Y. Y. Lin, A. T. Liu, J. Shi, X. Chang, G.-T. Lin, T.-H. Huang, W.-C. Tseng, K. tik Lee, D.-R. Liu, Z. Huang, S. Dong, S.-W. Li, S. Watanabe, A. Mohamed, and H. yi Lee, “SUPERB: Speech Processing Universal PERformance Benchmark,” in Proc. Interspeech 2021 , 2021, pp. 1194–1198
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Lin, Y. Wang, K. Kalgaonkar, G. Keren, D. Zhang, and C. Fuegen, “A time-domain convolutional recurrent network for packet loss concealment,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2021, pp. 7148–7152
2021
Later among the works it cites.
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Y. Gong, S. Khurana, A. Rouditchenko, and J. Glass, “Cmkd: Cnn/transformer-based cross-model knowledge distillation for audio classification,” 2022
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D. Chong, H. Wang, P. Zhou, and Q. Zeng, “Masked spectrogram prediction for self-supervised audio pre-training,” 2022
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Y. Li, C.-Y. Wu, H. Fan, K. Mangalam, B. Xiong, J. Malik, and C. Feichtenhofer, “Mvitv2: Improved multiscale vision transformers for classification and detection,” in CVPR , 2022
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