Automatic classification of playing techniques in Guitar Pro songs
Bibliographic record
Abstract
Automatic guitar transcription has been an active research area for decades.Existing work in this area has mostly focused on estimating the onset, offset, and pitch of note events.Another important aspect of expressive guitar performance, the use of playing techniques, is less studied.The system presented in this thesis is designed to recognize five common playing techniques performed on the electric guitar: bend, vibrato, hammeron, pull-off, and slide.The system has three steps.For a given audio track, the monophony detector extracts all the monophonic segments, where most playing technique instances occur.Then, the note-event separator splits monophonic audio segments into note events.Finally, machine learning techniques are used to classify their playing techniques using various audio features such as pitch, spectral centroid, and mel-frequency cepstral coefficients.The presented system is trained and evaluated on a novel dataset of 379 synthesized guitar solo recordings, mostly in the genre of rock and metal, generated using publicly available Guitar Pro files collected from tablature websites.As an extended guitar tablature format, a Guitar Pro file encodes information about every note event in a guitar track, such as the timestamp, pitch, fingering position, and playing techniques.Using the Guitar Pro software, a realistic guitar audio track can be generated from the Guitar Pro file.Its corresponding annotation can also be obtained by programmatically decoding the Guitar Pro file.The end-to-end testing experiments showed that the presented system can effectively recognize the five playing techniques in synthesized guitar solos, with per-class f1 scores ranging from 71.7% to 89.2%.I am also grateful to all my friends and colleagues at the Distributed Digital Music Archives and Libraries (DDMAL) Lab for their moral support and inspirations.Especially, thank you to Néstor Nápoles L ópez and Timothy de Reuse for editing my thesis proposal and sharing great ideas and conversations throughout my studies.Thank you to Sevag Hanssian and Geneviève Gates-Panneton for translating my thesis abstract into French.Finally, I would like to thank my parents for supporting
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".