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Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingerings rather than pitches.
C. Mckay and I. Fujinaga, “jSymbolic: A Feature Extractor for MIDI Files,” in Proc. of the International Computer Music Conference , 2006
2006
Earlier work this paper cites.
M. S. Cuthbert and C. Ariza, “music21: A Toolkit for Computer-Aided Musicology and Symbolic Music Data,” in Proc. of the 11th International Society for Music Information Retrieval Conference , 2010
2010
Earlier work this paper cites.
R. Macrae and S. Dixon, “Guitar Tab Mining, Analysis and Ranking,” in Proc. of the 12th International Society for Music Information Retrieval Conference , 2011
2011
Earlier work this paper cites.
M. Barthet, A. Anglade, G. Fazekas, S. Kolozali, and R. Macrae, “Music Recommendation for Music Learning: Hotttabs, a Multimedia Guitar Tutor,” in Workshop on Music Recommendation and Discovery , 2011, pp. 7–13
2011
Earlier work this paper cites.
A. Kotsifakos, E. E. Kotsifakos, P. Papapetrou, and V. Athitsos, “Genre Classification of Symbolic Music with SMBGT,” in Proc. of the 6th International Conference on PErvasive Technologies Related to Assistive Environments . New York, NY, USA: Association for Computing Machinery, 2013
2013
Earlier work this paper cites.
C. Raffel and D. P. W. Ellis, “Intuitive Analysis, Creation and Manipulation of MIDI Data with pretty_midi,” in Late-Breaking Demos of the 15th International Society for Music Information Retrieval Conference , 2014
2014
Earlier work this paper cites.
L. Su, L.-F. Yu, and Y.-H. Yang, “Sparse Cepstral, Phase Codes for Guitar Playing Technique Classification.” in Proc. of the 15th International Society for Music Information Retrieval Conference , 2014
2014
Earlier work this paper cites.
C. Kehling, J. Abeßer, C. Dittmar, and G. Schuller, “Automatic Tablature Transcription of Electric Guitar Recordings by Estimation of Score and Instrument-related Parameters,” in Proc. of the 17th Int. Conference on Digital Audio Effects , 2014
2014
Earlier work this paper cites.
C. Raffel and D. P. W. Ellis, “Extracting Ground Truth Information from MIDI Files: A MIDIfesto,” in Proc. of the 17th International Society for Music Information Retrieval Conference , 2016
2016
Earlier work this paper cites.
S. Merity, C. Xiong, J. Bradbury, and R. Socher, “Pointer Sentinel Mixture Models,” Proc. of the 5th International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
M. Moocarme, “Deep learning metallica with recurrent neural networks,” 2016. [Online]. Available: https://www.mattmoocar.me/blog/tabPlayer/
2016
Earlier work this paper cites.
D. C. Corrêa and F. A. Rodrigues, “A Survey on Symbolic Data-based Music Genre Classification,” Expert Systems with Applications , vol. 60, pp. 190–210, 2016
2016
Earlier work this paper cites.
A. Vaswani, G. Brain, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention Is All You Need,” in Proc. of the 31st Conference on Neural Information Processing Systems , 2017
2017
Earlier work this paper cites.
J. Abeßer and G. Schuller, “Instrument-centered music transcription of solo bass guitar recordings,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 25, no. 9, 2017, pp. 1741–1750
2017
Earlier work this paper cites.
J. Abeßer, S. Balke, K. Frieler, M. Pfleiderer, and M. Müller, “Deep Learning for Jazz Walking Bass Transcription,” in Proc. of the AES International Conference on Semantic Audio , 2017
2017
Earlier work this paper cites.
C. Donahue, H. H. Mao, and J. Mcauley, “The NES music database: A Multi-Instrumental Dataset with Expressive Performance Attributes,” in Proc. of the 19th International Society for Music Information Retrieval Conference , 2018
2018
Cited alongside, same era.
Q. Xi, R. M. Bittner, J. Pauwels, X. Ye, and J. P. Bello, “GuitarSet: A Dataset for Guitar Transcription,” in Proc. of the 19th International Society for Music Information Retrieval Conference , 2018
2018
Cited alongside, same era.
S. Rodríguez, E. Gómez, and H. Cuesta, “Automatic transcription of Flamenco guitar falsetas,” in Proc. International Workshop on Folk Music Analysis , 2018
2018
Cited alongside, same era.
G. Brunner, Y. Wang, R. Wattenhofer, and S. Zhao, “Symbolic Music Genre Transfer with CycleGAN,” in Proc. of the IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI) , 2018, pp. 786–793
2018
Cited alongside, same era.
Y.-N. Hung, I.-T. Chiang, Y.-A. Chen, and Y.-H. Yang, “Musical Composition Style Transfer via Disentangled Timbre Representations,” in Proc. of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 4697–4703
2019
Later among the works it cites.
OpenAI, “USPTO Comment Regarding Request for Comments on Intellectual Property Protection for Artificial Intelligence Innovation,” 2019. [Online]. Available: https://www.uspto.gov/sites/default/files/documents/OpenAI_RFC-84-FR-58141.pdf
2019
Later among the works it cites.
H. W. Dong, K. Chen, J. McAuley, and T. Berg-Kirkpatrick, “MusPY: A Toolkit for Symbolic Music Generation,” in Proc. of the 21st International Society for Music Information Retrieval, ISMIR , 2020
2020
Later among the works it cites.
Q. Kong, B. Li, J. Chen, and Y. Wang, “GiantMIDI-Piano: A Large-Scale MIDI Dataset for Classical Piano music,” in Transactions of the International Society for Music Information Retrieval , 2020
2020
Later among the works it cites.
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R. De Valk, R. Ahmed, and T. Crawford, “JosquIntab: A Dataset for Content-based Computational Analysis of Music in Lute Tablature,” in Proc. of the 20th International Society for Music Information Retrieval Conference , 2019
2019
Cited alongside, same era.
T. Magnusson, Sonic Writing: Technologies of Material, Symbolic & Signal Inscriptions . Bloomsbury Academic, 2019
2019
Cited alongside, same era.
C. Hawthorne, A. Stasyuk, A. Roberts, I. Simon, C.-Z. Anna Huang, S. Dieleman, E. Elsen, J. Engel, and D. Eck Google Brain, “Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset,” 2019
2019
Cited alongside, same era.
C. Payne, “Musenet,” 2019. [Online]. Available: openai.com/blog/musenet
2019
Cited alongside, same era.
C.-Z. Anna Huang and A. M. Vaswani Jakob Uszkoreit Noam Shazeer Ian Simon Curtis Hawthorne Andrew Dai Matthew D Hoffman Monica Dinculescu Douglas Eck Google Brain, “Music Transformer: Generating Music with Long-term Structure,” in Proc. of the 7th International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Waterman, “Musetree,” 2019. [Online]. Available: https://stevenwaterman.uk/musetree/
2019
Cited alongside, same era.
Z. Dai, Z. Yang, Y. Yang, J. Carbonell, Q. V. Le, and R. Salakhutdinov, “Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019
2019
Cited alongside, same era.
Z. Wang, K. Chen, J. Jiang, Y. Zhang, M. Xu, S. Dai, X. Gu, and G. Xia, “POP909: A Pop-Song Dataset for Music Arrangement Generation,” in Proc. of 21st International Conference on Music Information Retrieval , 2020
2020
Later among the works it cites.
Y.-H. Chen, Y.-H. Huang, W.-Y. Hsiao, and Y.-H. Yang, “Automatic Composition of Guitar Tabs by Transformers and Groove Modelling,” in Proc. of the 21st International Society for Music Information Retrieval Conference , 2020
2020
Later among the works it cites.
Y.-S. Huang and Y.-H. Yang, “Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions,” in Proc. of the 28th ACM International Conference on Multimedia , 2020
2020
Later among the works it cites.
O. Cífka, U. Şimşekli, and G. Richard, “Groove2Groove: One-Shot Music Style Transfer With Supervision From Synthetic Data,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, 2020, pp. 2638–2650
2020
Later among the works it cites.
Y.-Q. Lim, C. S. Chan, and F. Y. Loo, “Style-Conditioned Music Generation,” in 2020 IEEE International Conference on Multimedia and Expo (ICME) , 2020, pp. 1–6
2020
Later among the works it cites.
T. J. Tsai and K. Ji, “Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining,” in Proc. of the 21st International Society for Music Information Retrieval Conference , 2020
2020
Later among the works it cites.
S. Kim, H. Lee, S. Park, J. Lee, and K. Choi, “Deep Composer Classification Using Symbolic Representation,” in Late-Breaking Demo Session of the 21st International Society for Music Information Retrieval Conference , 2020
2020
Later among the works it cites.
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever, “Jukebox: A Generative Model for Music,” 2020. [Online]. Available: https://github.com/openai/jukebox
2020
Later among the works it cites.
W.-Y. Hsiao, J.-Y. Liu, Y.-C. Yeh, and Y.-H. Yang, “Compound Word Transformer: Learning to Compose Full-Song Music Over Dynamic Directed Hypergraphs,” in Proc. of the AAAI Conference on Artificial Intelligence , 2021
2021
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