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http://yp-chen.com/images/SoloLa_logo.png

SoloLa! is an automatic system for transforming lead guitar audio signal in music recording into sheet music, which features automatic guitar expression style recognition.

The system comprises of the following processing bloakcs:
  1. Source Separation - isolate the audio signal of guitar solo from mixture
  2. Melody Extraction - estimate the fundamental frequency corresponding to the pitch of the lead guitar to generate a series of consecutive pitch values which are continuous in both time and frequency, a.k.a. melody contour
  3. Note Tracking - track the estimated melody contour to recognize discrete musical note events
  4. Expression Style Recognition - the detection of applied lead guitar playing techniques such as string bend, slide and vibrato
  5. Fingering Arrangement - maps the sequence of notes to a set of guitar fretboard positions

https://github.com/srviest/SoloLa-/blob/master/solola_workflow.jpg

  • Melody contour plot reproduced from J. Salamon, E. Gómez, D. P. W. Ellis and G. Richard, "Melody Extraction from Polyphonic Music Signals: Approaches, Applications and Challenges", IEEE Signal Processing Magazine, 31(2):118-134, Mar. 2014 with permission from the authors.

Requirements

Author

Yuan-Ping Chen, Ting-Wei Su

References

[1]Zafar Rafii and Bryan Pardo, REpeating Pattern Extraction Technique (REPET): A Simple Method for Music/Voice Separation, IEEE Transactions on Audio, Speech, and Language Processing, 21(1):71--82, January 2013.
[2]Derry FitzGerald, Harmonic/Percussive Separation using Median Filtering, in Proc. of the International Conference on Digital Audio Effects (DAFx), 2010.
[3]J. Salamon and E. Gómez. Melody Extraction from Polyphonic Music Signals using Pitch Contour Characteristics, IEEE Transactions on Audio, Speech and Language Processing, 20(6):1759-1770, Aug. 2012.
[4]Gregory Burlet and Ichiro Fujinaga, Robotaba Guitar Tablature Transcription Framework, in Proc. of the 14th International Society for Music Information Retrieval Conference (ISMIR), 2013.
[5]M. Mauch and S. Dixon. pYIN: A Fundamental Frequency Estimator Using Probabilistic Threshold Distributions, in Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
[6]L. Su, L.-F. Yu and Y.-H. Yang. Sparse Cepstral and Phase Codes for Guitar Playing Technique Classification, in Proc. of the 15th International Society for Music Information Retrieval Conference (ISMIR), 2014.
[7]Y.-P. Chen, L. Su and Y.-H. Yang. Electric Guitar Playing Technique Detection in Real-World Recording Based on F0 Sequence Pattern Recognition, in Proc. of the 16th International Society for Music Information Retrieval Conference (ISMIR), 2015.
[8]J. Driedger and M. Müller. TSM Toolbox: MATLAB Implementations of Time-Scale Modification Algorithms, in Proc. of the International Conference on Digital Audio Effects (DAFx), 2014.
[9]B. McFee, E. Humphrey, and J.P. Bello, A software framework for musical data augmentation, in Proc. of the 16th International Society for Music Information Retrieval Conference (ISMIR), 2015.
[10]Florian Krebs, Sebastian Böck and Gerhard Widmer, An Efficient State Space Model for Joint Tempo and Meter Tracking, in Proc. of the 16th International Society for Music Information Retrieval Conference (ISMIR), 2015.
[11]Florian Krebs, Sebastian Böck and Gerhard Widmer, Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio, in Proc. of the 14th International Society for Music Information Retrieval Conference (ISMIR), 2013.