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The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL).
G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Transactions on speech and audio processing , vol. 10, no. 5, pp. 293–302, 2002
2002
Earlier work this paper cites.
E. Law, K. West, M. I. Mandel, M. Bay, and J. S. Downie, “Evaluation of algorithms using games: The case of music tagging.” in ISMIR , 2009, pp. 387–392
2009
Earlier work this paper cites.
M. Soleymani, M. N. Caro, E. M. Schmidt, C.-Y. Sha, and Y.-H. Yang, “1000 songs for emotional analysis of music,” in Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia , 2013, pp. 1–6
2013
Earlier work this paper cites.
P. Knees, Á. Faraldo Pérez, H. Boyer, R. Vogl, S. Böck, F. Hörschläger, M. Le Goff et al. , “Two data sets for tempo estimation and key detection in electronic dance music annotated from user corrections,” in Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR); 2015 Oct 26-30; Málaga, Spain.[Málaga]: International Society for Music Information Retrieval, 2015. p. 364-70. International Society for Music Information Retrieval (ISMIR), 2015
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Ott, S. Edunov, A. Baevski, A. Fan, S. Gross, N. Ng, D. Grangier, and M. Auli, “fairseq: A fast, extensible toolkit for sequence modeling,” in Proceedings of NAACL-HLT 2019: Demonstrations , 2019
2019
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2019
Cited alongside, same era.
2020
Cited alongside, same era.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al. , “Bootstrap your own latent-a new approach to self-supervised learning,” Advances in neural information processing systems , vol. 33, pp. 21 271–21 284, 2020
2020
Cited alongside, same era.
2021
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2021
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2022
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2022
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A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in Neural Information Processing Systems , vol. 33, pp. 12 449–12 460, 2020
2020
Cited alongside, same era.