BranchesTags

Repository files navigation

Fluid Flow — Vocal Fold Transfer Learning

Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.

Quick start

# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py

Scripts resolve all paths relative to their own location, so they can be run from any working directory.

Datasets:

  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features

CSVs are gitignored; rebuild from MATLAB or pull from your local data location.

Documentation

Operational (what we're doing right now — Ben + Callum + both Claudes)

DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence

Reference (what the project is)

DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)

Branches

main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.

Contributors

Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:

  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)

About

This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models

Topics

biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Fluid Flow — Vocal Fold Transfer Learning
Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.
Quick start
# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py
Scripts resolve all paths relative to their own location, so they can be run
from any working directory.
Datasets:
  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features
CSVs are gitignored; rebuild from MATLAB or pull from your local data location.
Documentation
Operational (what we're doing right now — Ben + Callum + both Claudes)
DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence
Reference (what the project is)
DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)
Branches
main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.
Contributors
Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:
  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)
About
This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models
Topics
biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Fluid Flow — Vocal Fold Transfer Learning
Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.
Quick start
# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py
Scripts resolve all paths relative to their own location, so they can be run
from any working directory.
Datasets:
  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features
CSVs are gitignored; rebuild from MATLAB or pull from your local data location.
Documentation
Operational (what we're doing right now — Ben + Callum + both Claudes)
DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence
Reference (what the project is)
DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)
Branches
main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.
Contributors
Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:
  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)
About
This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models
Topics
biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Fluid Flow — Vocal Fold Transfer Learning
Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.
Quick start
# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py
Scripts resolve all paths relative to their own location, so they can be run
from any working directory.
Datasets:
  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features
CSVs are gitignored; rebuild from MATLAB or pull from your local data location.
Documentation
Operational (what we're doing right now — Ben + Callum + both Claudes)
DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence
Reference (what the project is)
DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)
Branches
main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.
Contributors
Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:
  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)
About
This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models
Topics
biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Fluid Flow — Vocal Fold Transfer Learning
Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.
Quick start
# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py
Scripts resolve all paths relative to their own location, so they can be run
from any working directory.
Datasets:
  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features
CSVs are gitignored; rebuild from MATLAB or pull from your local data location.
Documentation
Operational (what we're doing right now — Ben + Callum + both Claudes)
DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence
Reference (what the project is)
DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)
Branches
main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.
Contributors
Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:
  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)
About
This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models
Topics
biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Fluid Flow — Vocal Fold Transfer Learning
Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.
Quick start
# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py
Scripts resolve all paths relative to their own location, so they can be run
from any working directory.
Datasets:
  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features
CSVs are gitignored; rebuild from MATLAB or pull from your local data location.
Documentation
Operational (what we're doing right now — Ben + Callum + both Claudes)
DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence
Reference (what the project is)
DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)
Branches
main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.
Contributors
Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:
  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)
About
This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models
Topics
biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Fluid Flow — Vocal Fold Transfer Learning
Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.
Quick start
# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py
Scripts resolve all paths relative to their own location, so they can be run
from any working directory.
Datasets:
  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features
CSVs are gitignored; rebuild from MATLAB or pull from your local data location.
Documentation
Operational (what we're doing right now — Ben + Callum + both Claudes)
DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence
Reference (what the project is)
DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)
Branches
main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.
Contributors
Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:
  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)
About
This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models
Topics
biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Fluid Flow — Vocal Fold Transfer Learning
Fast ML regressors that predict vocal-fold acoustic outputs (fundamental frequency F0, sound pressure level SPL) from three motor inputs: cricothyroid activation a_CT, thyroarytenoid activation a_TA, and subglottal pressure PS. The point: train on a cheap source model (BCM) and transfer-learn to expensive targets (TBCM, Beam-Membrane FEM) to reduce expensive simulations.
Quick start
# Python 3.13 is what we develop against; 3.10+ should work.
pip install scikit-learn torch tensorflow pandas numpy matplotlib joblib scipy
# TabPFN scripts additionally need the cloud client (free account + token):# pip install tabpfn-client# export TABPFN_TOKEN=<your token> # PowerShell: $env:TABPFN_TOKEN = "<token>"# Set TABPFN_FORCE_LOCAL=1 to use the local `tabpfn` package instead.# --- Original female-BCM transfer (Ben) ---
python "VocalFoldRegression/BCM Model/RandomForest/MaleRF.py"
python "VocalFoldRegression/BCM Model/RandomForest/FemaleRFTransfer.py"# --- BCM → BM transfer (Callum) ---
MPLBACKEND=Agg python Beam_Membrane/BM_TransferRF.py # 6 RF methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_TransferAE.py # 3 autoencoder methods (Callum)
MPLBACKEND=Agg python Beam_Membrane/BM_SmallData.py # 10–500 sample sweep (Callum)# --- Non-transfer alternates (Ben) ---
MPLBACKEND=Agg python Beam_Membrane/BM_GP.py # Gaussian Process baseline
MPLBACKEND=Agg python Beam_Membrane/BM_TabPFN.py # Pretrained tabular foundation model# --- Comparison summary (reads all of the above) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Summary.py # cross-method comparison + figures
MPLBACKEND=Agg python Beam_Membrane/BM_Showcase.py # presentation-quality figures: headline, sim-budget, bootstrap# --- BCM → TBCM transfer (Callum) ---
MPLBACKEND=Agg python TBCM/TBCM_TransferRF.py
MPLBACKEND=Agg python TBCM/TBCM_Autoencoder.py
MPLBACKEND=Agg python TBCM/TBCM_Summary.py
# --- Extended BM outputs (adds ACFL and friends beyond F0/SPL) ---
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TransferRF.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_TabPFN.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Runtime.py
MPLBACKEND=Agg python Beam_Membrane/BM_Extended_Compare.py # reads the three above# --- JASA-target comparison (TabPFN vs TransferRF, multihead vs single) ---
MPLBACKEND=Agg python JASA/JASA_TransferRF.py
MPLBACKEND=Agg python JASA/JASA_TabPFN.py
MPLBACKEND=Agg python JASA/JASA_Compare.py
# --- TBCM motor-control maps (sparse-data replication of JASA Fig. 6) ---
MPLBACKEND=Agg python TBCM/TBCM_MotorMaps.py
Scripts resolve all paths relative to their own location, so they can be run
from any working directory.
Datasets:
  • VocalFoldRegression/BCM Model/MaleBCM.csv — ~54k male BCM samples
  • VocalFoldRegression/BCM Model/FemaleBCM.csv — female BCM (filtered ACFL > 30)
  • Beam_Membrane/dataset_BM.csv — ~5,000 BM simulations (generated by Generate_BM_Dataset.m)
  • TBCM/dataset_TBCM.csv — ~43k TBCM samples
  • TBCM/dataset_TBCM_enriched.csv — TBCM + waveform features
CSVs are gitignored; rebuild from MATLAB or pull from your local data location.
Documentation
Operational (what we're doing right now — Ben + Callum + both Claudes)
DocPurpose
team/TODO.mdMaster task list with owner + status. Single source of truth for "what is there to do"
team/BOARD.mdKanban view (Backlog / In Progress / Review / Recently Done)
team/MEETING_NOTES.mdAppend-only log of ~1pm syncs
team/README.mdFolder conventions, owner / status / priority values, cadence
Reference (what the project is)
DocPurpose
CLAUDE.mdEntry point, conventions, repo map (auto-loaded by Claude)
docs/ARCHITECTURE.mdSystem design, regressor matrix, transfer strategies (incl. autoencoder methods)
docs/MILESTONES.mdDated history of what's shipped
docs/ROADMAP.mdStrategic research phases (multi-month)
docs/GLOSSARY.mdDomain terms, methods, file references
docs/DECISIONS.mdAppend-only judgment log
PROJECT_GUIDE.mdCallum's hands-on guide for Beam_Membrane/ and TBCM/ (his standalone notes)
Branches
main and feature/fem are aligned. Work continues on feature/fem and is fast-forwarded to main after sync points.
Contributors
Equal collaborators; per-task ownership tracked in team/TODO.md. Original authorship of code areas:
  • Ben Gladney — VocalFoldRegression/ (male/female BCM, RF/NN/PR baselines and transfer)
  • Callum Camazzola — Beam_Membrane/, TBCM/ (BCM → BM and BCM → TBCM transfer; RF and autoencoder methods)
About
This project implements a machine-learning regression model that predicts key acoustic outputs of various vocal fold models
Topics
biomechanicsmachine-learningpythonregressionresearchscientific-computingvocal-folds
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages