Leveraging Computer Vision Networks for Guitar Tablature Transcription
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50069-5_2
Reference26 articles.
1. Baevski, A., Auli, M.: Adaptive input representations for neural language modeling. arXiv preprint arXiv:1809.10853 (2018)
2. Barbancho, A.M., Klapuri, A., Tardon, L.J., Barbancho, I.: Automatic transcription of guitar chords and fingering from audio. IEEE Trans. Audio Speech Lang. Process. 20(3), 915–921 (2012). https://doi.org/10.1109/TASL.2011.2174227
3. Barbancho, I., Tardon, L.J., Sammartino, S., Barbancho, A.M.: Inharmonicity-based method for the automatic generation of guitar tablature. IEEE Trans. Audio Speech Lang. Process. 20(6), 1857–1868 (2012). https://doi.org/10.1109/TASL.2012.2191281
4. Bayoudh, K., Knani, R., Hamdaoui, F., Mtibaa, A.: A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. The Visual Computer, pp. 1–32 (2021)
5. Cwitkowitz, F., Hirvonen, T., Klapuri, A.: Fretnet: Continuous-valued pitch contour streaming for polyphonic guitar tablature transcription. arXiv preprint arXiv:2212.03023 (2022)
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