[1] Alonso-Jiménez, P., Serra, X., and Bogdanov, D. (2022). Music representation learning based on editorial metadata from discogs.
[2] Baevski, A., Zhou, Y., Mohamed, A., and Auli, M. (2020). wav2vec 2.0: A framework for self-supervised learning of speech representations. Advances in neural information processing systems, 33:12449-12460.
[3] Bertin-Mahieux, T., Ellis, D. P., Whitman, B., and Lamere, P. (2011). The million song dataset.
[4] Bittner, R., Salamon, J., Tierney, M., Mauch, M., Cannam, C., and Bello, J. (2014). Medleydb: A multitrack dataset for annotation-intensive mir research.
[5] Böck, S., Korzeniowski, F., Schlüter, J., Krebs, F., and Widmer, G. (2016a). madmom: a new Python Audio and Music Signal Processing Library. In Proceedings of the 24th ACM International Conference on Multimedia, pages 1174-1178, Amsterdam, The Netherlands.
[6] Böck, S., Krebs, F., and Widmer, G. (2016b). Joint beat and downbeat tracking with recurrent neural networks. In ISMIR, pages 255-261. New York City.
[7] Bogdanov, D., Won, M., Tovstogan, P., Porter, A., and Serra, X. (2019). The mtg-jamendo dataset for automatic music tagging. In International Conference on Machine Learning. ICML.
[8] Castellon, R., Donahue, C., and Liang, P. (2021). Codified audio language modeling learns useful representations for music information retrieval. arXiv preprint arXiv:2107.05677.
[9] Dai, S., Zhang, Z., and Xia, G. G. (2018). Music style transfer: A position paper. arXiv preprint arXiv:1803.06841.
[10] DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., and Wallace, B. C. (2020). Eraser: A benchmark to evaluate rationalized nlp models. Transactions of the Association for Computational Linguistics.
[11] Dhariwal, P., Jun, H., Payne, C., Kim, J. W., Radford, A., and Sutskever, I. (2020). Jukebox: A generative model for music. arXiv preprint arXiv:2005.00341.
[12] Durand, S., Stoller, D., and Ewert, S. (2023a). Contrastive learning-based audio to lyrics alignment for multiple languages. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5. IEEE.
[13] Durand, S., Stoller, D., and Ewert, S. (2023b). Contrastive learning-based audio to lyrics alignment for multiple languages. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1-5.
[14] Engel, J., Resnick, C., Roberts, A., Dieleman, S., Norouzi, M., Eck, D., and Simonyan, K. (2017). Neural audio synthesis of musical notes with wavenet autoencoders. In International Conference on Machine Learning, pages 1068-1077. PMLR.
[15] Gao, X., Gupta, C., and Li, H. (2023). Polyscriber: Integrated fine-tuning of extractor and lyrics transcriber for polyphonic music. IEEE ACM Trans. Audio Speech Lang. Process., 31:1968-1981.
[16] Goyal, P., Mahajan, D., Gupta, A., and Misra, I. (2019). Scaling and benchmarking self-supervised visual representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6391-6400.
[17] Graves, A., Fernández, S., Gomez, F. J., and Schmidhuber, J. (2006). Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In Proc. ICML, volume 148, pages 369-376. ACM.
[18] Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., and Smith, N. A. (2020). Don't stop pretraining: Adapt language models to domains and tasks. arXiv preprint arXiv:2004.10964.