Skip to main content
Have a personal or library account? Click to login
Rhythm Guitar Tablature Continuation Through Picking Pattern Generation Cover

Rhythm Guitar Tablature Continuation Through Picking Pattern Generation

Open Access
|Aug 2026

References

  1. Adkins, S., Sarmento, P., and Barthet, M. (2023). LooperGP: A loopable sequence model for live coding performance using GuitarPro tablature. In Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2023, pp. 319. Springer. 10.1007/978-3-031-29956-8_1.
  2. Bacot, B., Navarret, B., and Bigo, L. (2026). An exploratory user study of tablature software: Perspectives on popular music creativity with algorithmic tools. M. Lovett, J.‑O. Gullö, J. Paterson, R. Toulson, and R. Hepworth‑Sawyer (Eds.), Innovation in Music. Routledge. https://hal.science/hal-05426342.
  3. Bimbot, F., Deruty, E., Sargent, G., and Vincent, E. (2016). System & contrast. Music Perception, 33(5), 631661. https://online.ucpress.edu/mp/article-abstract/33/5/631/92007/System-amp-ContrastA-Polymorphous-Model-of-the?redirectedFrom=fulltext.
  4. Braun, V., and Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77101. 10.1191/1478088706qp063oa.
  5. Chen, H., Smith, J. B. L., Spijkervet, J., Wang, J.‑C., Zou, P., Li, B., Kong, Q., and Du, X. (2024). SymPAC: Scalable symbolic music generation with prompts and constraints. In Proceedings of the 25th International Society for Music Information Retrieval Conference, pp. 10291036. 10.5281/zenodo.14877507.
  6. Chen, K., Wang, C.‑I., Berg‑Kirkpatrick, T., and Dubnov, S. (2020a). Music Sketchnet: Controllable music generation via factorized representations of pitch and rhythm. In Proceedings of the 21st International Society for Music Information Retrieval Conference, pp. 7784. 10.48550/arXiv.2008.01291.
  7. Chen, Y.‑H., Huang, Y.‑S., Hsiao, W.‑Y., and Yang, Y.‑H. (2020b). Automatic composition of guitar tabs by Transformers and groove modeling. In Proceedings of the 21st International Society for Music Information Retrieval Conference, pp. 756763. 10.48550/arXiv.2008.01431.
  8. Choi, K., Park, J., Heo, W., Jeon, S., and Park, J. (2021). Chord conditioned melody generation with Transformer based decoders. IEEE Access, 9, 4207142080. 10.1109/ACCESS.2021.3065831.
  9. Couturier, L., Bigo, L., and Levé, F. (2023). Comparing texture in piano scores. In Proceedings of the 24th International Society for Music Information Retrieval Conference, pp. 508515. 10.5281/zenodo.10265337.
  10. Dahale, R., Talwadker, V., Rao, P., and Verma, P. (2022). Generating coherent drum accompaniment with fills and improvisations. In Proceedings of the 23rd International Society for Music Information Retrieval Conference, pp. 264271. 10.48550/arXiv.2209.00291.
  11. Dahia, M., Santana, H., Trajano, E., Ramalho, G., Sandroni, C., and Cabral, G. (2004). Using patterns to generate rhythmic accompaniment for guitar. In Actes des Journées d’Informatique Musicale. https://hal.science/hal-03354323.
  12. Dai, S., Jin, Z., Gomes, C., and Dannenberg, R. B. (2021). Controllable deep melody generation via hierarchical music structure representation. In Proceedings of the 22nd International Society for Music Information Retrieval Conference, pp. 143150. 10.48550/arXiv.2109.00663.
  13. Dalmazzo, D., Déguernel, K., and Sturm, B. L. T. (2024). The Chordinator: Modeling music harmony by implementing Transformer networks and token strategies. In Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024, pp. 5266. Springer. 10.1007/978-3-031-56992-0_4.
  14. D’Hooge, A. (2025). Assisting Western popular music guitar practice and tablature composition with machine learning. PhD thesis, Université de Lille. https://theses.hal.science/tel-05466245v1.
  15. D’Hooge, A., Bigo, L., Déguernel, K., and Martin, N. (2024). Guitar chord diagram suggestion for Western popular music. In Proceedings of the 21st Sound and Music Computing Conference, pp. 8288. 10.5281/zenodo.14336096.
  16. D’Hooge, A., Déguernel, K., and Bigo, L. (2026). Rhythm guitar tablature continuation through picking pattern generation ‑ data [Data set]. Zenodo. 10.5281/zenodo.21304818.
  17. Dong, H.‑W., Hsiao, W.‑Y., Yang, L.‑C., and Yang, Y.‑H. (2018). MuseGAN: Multi‑track sequential generative adversarial networks for symbolic music generation and accompaniment. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 3441. 10.1609/aaai.v32i1.11312.
  18. Ens, J., and Pasquier, P. (2020). MMM: Exploring conditional multi‑track music generation with the Transformer. 10.48550/arXiv.2008.06048 Preprint.
  19. Faul, F., Erdfelder, E., Lang, A.‑G., and Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175191. 10.3758/BF03193146.
  20. Gao, S., Lei, S., Zhuo, F., Liu, H., Liu, F., Tang, B., Huang, Q., Kang, S., and Wu, Z. (2024). An end‑to‑end approach for chord‑conditioned song generation. In Proceedings of the 25th Interspeech Conference, pp. 18901894. 10.21437/Interspeech.2024-1837.
  21. Giraud, M., Levé, F., Mercier, F., Rigaudière, M., and Thorez, D. (2014). Towards modeling texture in symbolic data. In Proceedings of the 15th International Society for Music Information Retrieval Conference, pp. 5964. https://hal.science/hal-01057017/.
  22. Hadjeres, G., and Crestel, L. (2021). The piano inpainting application. 10.48550/arXiv.2107.05944.
  23. Holzapfel, A., Kaila, A.‑K., and Jääskeläinen, P. (2024). Green MIR? Investigating computational cost of recent music‑AI research in ISMIR. In Proceedings of the 25th International Society for Music Information Retrieval Conference, pp. 371380. 10.5281/zenodo.14877351.
  24. Huang, C.‑Z. A., Vaswani, A., Uszkoreit, J., Shazeer, N., Simon, I., Hawthorne, C., Dai, A. M., Hoffman, M. D., Dinculescu, M., and Eck, D. (2019). Music Transformer: Generating music with long‑term structure. In Proceedings of the 7th International Conference on Learning Representations. 10.48550/arXiv.1809.04281.
  25. Huang, Y.‑S., and Yang, Y.‑H. (2020). Pop Music Transformer: Beat‑based modeling and generation of expressive pop piano compositions. In MM ‘20: Proceedings of the 28th ACM International Conference on Multimedia, pp. 11801188. 10.1145/3394171.3413671.
  26. Lattner, S., and Grachten, M. (2019). High‑level control of drum track generation using learned patterns of rhythmic interaction. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 3539. 10.1109/WASPAA.2019.8937261.
  27. Makris, D., Zixun, G., Kaliakatsos‑Papakostas, M., and Herremans, D. (2022). Conditional drums generation using compound word representations. In Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2022, pp. 179194. Springer. 10.1007/978-3-031-03789-4_12.
  28. Malandro, M. E. (2024). Composer’s Assistant 2: Interactive multi‑track MIDI infilling with fine‑grained user control. In Proceedings of the 25th International Society for Music Information Retrieval Conference, pp. 438445. 10.5281/zenodo.14877367.
  29. Margulis, E. H. (2014). On repeat: How music plays the mind. Oxford University Press.
  30. McVicar, M., Fukayama, S., and Goto, M. (2014). AutoRhythmGuitar: Computer‑aided composition for rhythm guitar in the tab space. In Proceedings of the 40th International Computer Music Conference joint with the 11th Sound and Music Computing Conference, pp. 293300. http://smc.afim-asso.org/smc-icmc-2014/papers/images/VOL_1/0293.pdf.
  31. Middleton, R., and Manuel, P. (2001). Popular music. In Oxford Music Online. Oxford University Press. 10.1093/gmo/9781561592630.article.43179.
  32. Nemeroff, B. (2024). Lead vs. rhythm guitar: What’s the difference? Fender. https://www.fender.com/articles/instruments/lead-vs-rhythm-guitar.
  33. Nistal, J., Pasini, M., Aouameur, C., Grachten, M., and Lattner, S. (2024a). Diff‑a‑Riff: Musical accompaniment co‑creation via latent diffusion models. In Proceedings of the 25th International Society for Music Information Retrieval Conference, pp. 272280. 10.5281/zenodo.14877327.
  34. Nistal, J., Pasini, M., and Lattner, S. (2024b). Improving musical accompaniment co‑creation via diffusion Transformers. In Audio Imagination: NeurIPS 2024 Workshop on AI‑Driven Speech, Music, and Sound Generation. https://openreview.net/forum?id=zyE3Kdd85t.
  35. Parker, J. D., Spijkervet, J., Kosta, K., Yesiler, F., Kuznetsov, B., Wang, J.‑C., Avent, M., Chen, J., and Le, D. (2024). STEMGEN: A music generation model that listens. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 11161120. 10.1109/ICASSP48485.2024.10446088.
  36. Pasini, M., Grachten, M., and Lattner, S. (2024). Bass accompaniment generation via latent diffusion. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 11661170. 10.1109/ICASSP48485.2024.10446400.
  37. Pinheiro, J., and Bates, D. (2000). Linear mixed‑effects models: Basic concepts and examples. In Mixed‑effects models in S and S‑PLUS, pp. 356. Springer. 10.1007/0-387-22747-4_1.
  38. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf.
  39. Régnier, D., Martin, N., and Bigo, L. (2021). Identification of rhythm guitar sections in symbolic tablatures. In Proceedings of the 22nd International Society for Music Information Retrieval Conference, pp. 5865. https://hal.science/hal-03335822/.
  40. Ren, Y., He, J., Tan, X., Qin, T., Zhao, Z., and Liu, T.‑Y. (2020). PopMAG: Pop music accompaniment generation. In MM ‘20: Proceedings of the 28th ACM International Conference on Multimedia, pp. 11981206. 10.1145/3394171.3413671.
  41. Row, E., Tang, J., and Fazekas, G. (2023). JAZZVAR: A dataset of variations found within solo piano performances of jazz standards for music overpainting. In Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research, pp. 113126. 10.1007/978-3-032-02042-0_8.
  42. Sakai, S., Segawa, H., and Kitahara, T. (2024). Tablature generation from lead sheets for finger‑style solo guitar. In Proceedings of the 21st Sound and Music Computing Conference, pp. 187190. https://smcnetwork.org/smc2024/papers/SMC2024_paper_id55.pdf.
  43. Sarmento, P. (2024). Guitar tablature generation with deep learning [PhD thesis, Queen Mary Univer‑ 1200 sity of London. https://qmro.qmul.ac.uk/xmlui/handle/123456789/98872.
  44. Sarmento, P., Kumar, A., Carr, C. J., Zukowski, Z., Barthet, M., and Yang, Y.‑H. (2021). DadaGP: A dataset of tokenized GuitarPro songs for sequence models. In Proceedings of the 22nd International Society for Music Information Retrieval Conference, pp. 610617. https://archives.ismir.net/ismir2021/paper/000076.pdf.
  45. Sarmento, P., Kumar, A., Chen, Y.‑H., Carr, C. J., Zukowski, Z., and Barthet, M. (2023a). GTR‑CTRL: Instrument and genre conditioning for guitar‑focused music generation with Transformers. In Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2023, pp. 260275. Springer. 10.1007/978-3-031-29956-8_17.
  46. Sarmento, P., Kumar, A., Xie, D., Carr, C. J., Zukowski, Z., and Barthet, M. (2023b). ShredGP: Guitarist style‑conditioned tablature generation with Transformers. In Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research, pp. 110121. 10.5281/zenodo.10110154.
  47. Schwartz, R., Dodge, J., Smith, N. A., and Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 5463. 10.1145/3381831.
  48. Vásquez, M. V., Baelemans, M. C. E., Driedger, J., and Burgoyne, J. A. (2025). Fretboardflow: A dual‑model approach to optimize chord voicings on the guitar fretboard. In Proceedings of the 26th International Society for Music Information Retrieval Conference, pp. 763770. 10.5281/zenodo.17706588.
  49. Wortman, K. A., and Smith, N. (2021). CombinoChord: A guitar chord generator app. In IEEE 11th Annual Computing and Communication Workshop and Conference, pp. 785789. 10.1109/CCWC51732.2021.9376001.
  50. Wu, S.‑L., and Yang, Y.‑H. (2023). MuseMorphose: Full‑song and fine‑grained piano music style transfer with one Transformer VAE. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 19531967. 10.1109/TASLP.2023.3270726.
  51. Wu, S.‑L., Zhu, G., Caceres, J.‑P., Huang, C.‑Z. A., and Bryan, N. J. (2026). Stemphonic: All‑at‑once flexible multi‑stem music generation. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1506715071. 10.1109/ICASSP55912.2026.11464473.
  52. Zhu, H., Liu, Q., Yuan, N. J., Qin, C., Li, J., Zhang, K., Zhou, G., Wei, F., Xu, Y., and Chen, E. (2018). XiaoIce Band: A melody and arrangement generation framework for pop music. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 28372846. 10.1145/3219819.3220105.
DOI: https://doi.org/10.5334/tismir.368 | Journal eISSN: 2514-3298
Language: English
Page range: 474 - 490
Submitted on: Jan 30, 2026
Accepted on: Jun 27, 2026
Published on: Aug 10, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services

© 2026 Alexandre D’Hooge, Ken Déguernel, Louis Bigo, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.