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<title>Leveraging Electric Guitar Tones and Effects to Improve Robustness in Guitar Tablature Transcription Modeling</title>
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<h1 class="title is-1 publication-title">Leveraging Electric Guitar Tones and Effects to Improve Robustness in Guitar Tablature Transcription Modeling</h1>
<div class="author-block">
<a href="mailto:hepv12@gmail.com" target="_blank">Hegel Pedroza</a><sup>1</sup>,
</div>
<div class="author-block">
<a href="mailto:wallace.abreu@smt.ufrj.br" target="_blank">Wallace Abreu</a><sup>2</sup>,
</div>
<div class="author-block">
<a href="mailto:corey1@uillinois.edu" target="_blank">Ryan M. Corey</a><sup>3</sup>,
</div>
<div class="author-block">
<a href="mailto:iran@ccrma.stanford.edu" target="_blank">Iran. R. Roman</a><sup>4</sup>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block" style="font-size: smaller;">
1. National Autonomous University of Mexico<br>
2. Federal University of Rio de Janeiro<br>
3. Discovery Partners Institute & University of Illinois Chicago<br>
4. New York University
</span>
<!-- <br>International Conference on Digital Audio Effects (DAFx), 2024 -->
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<h2 class="title is-3">In construction</h2>
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Under review for DAFx 2024; come back soon for more news.
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Guitar tablature transcription (GTT) aims at automatically generating symbolic representations from real solo guitar performances.
Due to its applications in education and musicology, GTT has gained traction in recent years.
However, GTT robustness has been limited due to the small size of available datasets.
Researchers have recently used synthetic data that simulates guitar performances using pre-recorded or computer-generated tones and can be automatically generated at large scales.
The present study complements these efforts by demonstrating that GTT robustness can be improved by including synthetic training data created using recordings of real guitar tones played with different audio effects.
We evaluate our approach on a new evaluation dataset with professional solo guitar performances that we composed and collected, featuring a wide array of tones, chords, and scales.
</p>
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<h2 class="title is-3">Training Data</h2>
<div class="content has-text-justified">
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</div>
<!-- Training data info -->
<p>Our synthetic guitar solo performances, melodies and chords consist of notes played
with varied guitar tones and audio effects, maximizing tone diversity. We achieve this by
randomly selecting tones from a vast bank of examples when generating an audio track.
This strategy aligns with our hypothesis that such diversity will enhance the model’s robustness.
It allows the model to concentrate on pitch content and guitar string+fret inference, while disregarding specific timbre qualities.
Hear a comparison of GuitarSet to GuitarSetFx and GuitarProFX below and see the CQT spectrogram of their early seconds!</p>
<br><br>
</br></br>
<div class="audio-container">
<div class="audio-item">
<p>GuitarSet Track</p>
<div class="container d-flex justify-content-center align-items-center">
<img src="static\images\GuitarSet_00_BN1-129-Eb_comp.jams.png" alt="Tabs results" class="img-fluid">
</div>
<audio controls>
<source src="static\audios\00_BN1-129-Eb_comp_mic_GuitarSet.wav" type="audio/mpeg">
Your browser does not support the audio element.
</audio>
</div>
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<p>GuitarSetFx track</p>
<div class="container d-flex justify-content-center align-items-center">
<img src="static\images\MyGuitarSetFx_00_BN1-129-Eb_comp.jams.png" alt="Tabs results" class="img-fluid">
</div>
<audio controls>
<source src="static\audios\00_BN1-129-Eb_comp_MyGuitarSet.wav" type="audio/mpeg">
Your browser does not support the audio element.
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<p>GuitarProFx Track</p>
<div class="container d-flex justify-content-center align-items-center">
<img src="static\images\Test6_00_1_mic.jams.png" alt="Tabs results" class="img-fluid">
</div>
<audio controls>
<source src="static\audios\00_1_mic.wav" type="audio/mpeg">
Your browser does not support the audio element.
</audio>
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<h2 class="title is-3">EGSet12</h2>
<div class="content has-text-justified">
</div>
</div>
</div>
<p>EGSet12 is a new evaluation set with
twelve original solo electric guitar performances (31.65 seconds
avg. duration, totaling 379.8 seconds). These pieces were composed by a professional musician and guitar player for this project,
showcasing the full tonal range of the electric guitar across diverse
melodies and chord complexities. EGSet12 encompasses a broad
spectrum of styles, including pop, funk, jazz and twelve-tone, reflecting varied tonalities, keys, rhythms, and modes. Hear some samples below!</p>
<br><br>
</br></br>
<div class="audio-container">
<div class="audio-item">
<p>Track 02</p>
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<source src="static\audios\00_02.wav" type="audio/mpeg">
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</audio>
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<p>Track 09</p>
<audio controls>
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<h2 class="title is-3">Results</h2>
</div>
</br></br>
<p class="columns is-centered has-text-centered">The following results show increased model robustness on multi-pitch and tablature prediction
metrics via our proposed method.</p>
</br></br>
<table class="table table-bordered table-striped">
<thead class="thead-dark">
<tr>
<th></th>
<th colspan="3" class="text-center">Multi-pitch estimation</th>
<th></th> <!-- This empty header shifts the Tablature estimation title to the right -->
<th colspan="4" class="text-center">Tablature estimation</th>
</tr>
<tr>
<th></th>
<th class="text-center">F<sub>1</sub></th>
<th class="text-center">P</th>
<th class="text-center">R</th>
<th></th> <!-- This empty header aligns with the previous row -->
<th class="text-center">F<sub>1</sub></th>
<th class="text-center">P</th>
<th class="text-center">R</th>
<th class="text-center">TDR</th>
</tr>
</thead>
<tbody>
<tr>
<td>TabCNN<sup><a href="#wiggins2019guitar">[1]</a></sup></td>
<td class="text-center">0.638±0.060</td>
<td class="text-center">0.819±0.080</td>
<td class="text-center">0.530±0.067</td>
<td></td>
<td class="text-center">0.447±0.071</td>
<td class="text-center">0.565±0.089</td>
<td class="text-center">0.375±0.067</td>
<td class="text-center">0.695±0.075</td>
</tr>
<tr>
<td>+ GuitarSetFX</td>
<td class="text-center"><strong>0.740±0.055</strong></td>
<td class="text-center">0.835±0.085</td>
<td class="text-center"><strong>0.679±0.052</strong></td>
<td></td>
<td class="text-center">0.557±0.088</td>
<td class="text-center">0.619±0.100</td>
<td class="text-center">0.518±0.084</td>
<td class="text-center">0.755±0.106</td>
</tr>
<tr>
<td>+ GuitarProFX</td>
<td class="text-center">0.719±0.061</td>
<td class="text-center"><strong>0.839±0.082</strong></td>
<td class="text-center">0.647±0.068</td>
<td></td>
<td class="text-center"><strong>0.585±0.084</strong></td>
<td class="text-center"><strong>0.658±0.073</strong></td>
<td class="text-center"><strong>0.541±0.087</strong></td>
<td class="text-center"><strong>0.819±0.075</strong></td>
</tr>
</tbody>
</table>
<p><strong>Table 1:</strong> TabCNN performance on EGSet12. Each cell is metric averaged across the twelve tracks (± denotes standard deviation). Top row: performance as trained by Wiggins & Kim. Bottom rows: performance when training data includes simulated tracks.</p>
</div>
</br></br>
<div class="columns is-centered has-text-centered">
<div class="container d-flex justify-content-center align-items-center">
<img src="static\images\results.svg" alt="Tabs results" class="img-fluid">
<p><strong>Figure 1:</strong> Each column is a two-second EGSet12 excerpt, comparing predictions made by models trained using GuitarSet, with and without GuitarProFX, against ground truth. Each circle is a tracked note on the guitar fretboard over time and vertical lines indicate musical beats.</p>
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In the video we illustrate the performance of TabCNN trained with GuitarSet versus our method, using GuitarSet + GuitarProFx, when evaluated on EGSet12.
</h2>
<video poster="" id="tree" autoplay controls muted loop height="100%">
<!-- Your video here -->
<source src="static\videos\tabs.mp4"
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