Skip to content
BranchesTags

Latest commit

History

14 Commits

Folders and files

NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py

Repository files navigation

Audio Chime

Paper Implementation for:

A convolutional neural network approach for acoustic scene classification [Paper]

We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.

About the Dataset

The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.

Cloning the repo

Go ahead and clone this repository using

$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run

If you are looking for a quick running version go inside single_file folder and run

$ python mainfile.py

Detailed Task

The process involves three steps:

  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset

Feature Extraction

We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.

Training

We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file. All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run

$ python mainfile.py

This will run the model which we test and use EER for rating our model.

About

📣 Audio tagging using deep models on CHIME-2016 dataset

Topics

audio-processingdeep-learning

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

2 watching

Forks

1 fork
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" + '
GitHub - DeepLearn-lab/audio_CHIME: :mega: Audio tagging using deep models on CHIME-2016 dataset · GitHub
Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
Audio Chime
Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
About the Dataset
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Cloning the repo
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py

Detailed Task
The process involves three steps:
  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset
Feature Extraction
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
Training
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py 
This will run the model which we test and use EER for rating our model.
About
📣 Audio tagging using deep models on CHIME-2016 dataset
Topics
audio-processingdeep-learning
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
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('^' + ".*" + '
GitHub - DeepLearn-lab/audio_CHIME: :mega: Audio tagging using deep models on CHIME-2016 dataset · GitHub
Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
Audio Chime
Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
About the Dataset
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Cloning the repo
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py

Detailed Task
The process involves three steps:
  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset
Feature Extraction
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
Training
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py 
This will run the model which we test and use EER for rating our model.
About
📣 Audio tagging using deep models on CHIME-2016 dataset
Topics
audio-processingdeep-learning
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
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('^' + ".*" + '
GitHub - DeepLearn-lab/audio_CHIME: :mega: Audio tagging using deep models on CHIME-2016 dataset · GitHub
Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
Audio Chime
Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
About the Dataset
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Cloning the repo
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py

Detailed Task
The process involves three steps:
  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset
Feature Extraction
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
Training
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py 
This will run the model which we test and use EER for rating our model.
About
📣 Audio tagging using deep models on CHIME-2016 dataset
Topics
audio-processingdeep-learning
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
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" + '
GitHub - DeepLearn-lab/audio_CHIME: :mega: Audio tagging using deep models on CHIME-2016 dataset · GitHub
Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
Audio Chime
Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
About the Dataset
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Cloning the repo
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py

Detailed Task
The process involves three steps:
  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset
Feature Extraction
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
Training
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py 
This will run the model which we test and use EER for rating our model.
About
📣 Audio tagging using deep models on CHIME-2016 dataset
Topics
audio-processingdeep-learning
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
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('^' + ".*" + '
GitHub - DeepLearn-lab/audio_CHIME: :mega: Audio tagging using deep models on CHIME-2016 dataset · GitHub
Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
Audio Chime
Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
About the Dataset
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Cloning the repo
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py

Detailed Task
The process involves three steps:
  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset
Feature Extraction
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
Training
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py 
This will run the model which we test and use EER for rating our model.
About
📣 Audio tagging using deep models on CHIME-2016 dataset
Topics
audio-processingdeep-learning
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
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('^' + ".*" + '
GitHub - DeepLearn-lab/audio_CHIME: :mega: Audio tagging using deep models on CHIME-2016 dataset · GitHub
Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
Audio Chime
Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
About the Dataset
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Cloning the repo
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py

Detailed Task
The process involves three steps:
  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset
Feature Extraction
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
Training
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py 
This will run the model which we test and use EER for rating our model.
About
📣 Audio tagging using deep models on CHIME-2016 dataset
Topics
audio-processingdeep-learning
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
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); }
})();
})();
GitHub - DeepLearn-lab/audio_CHIME: :mega: Audio tagging using deep models on CHIME-2016 dataset · GitHub
Skip to content
BranchesTags
Latest commit
History
14 Commits
Folders and files
NameName
Last commit message
Last commit date
single_file
single_file
README.md
README.md
mainfile.py
mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
Audio Chime
Paper Implementation for:
A convolutional neural network approach for acoustic scene classification [Paper]
We have worked on the CHIME Audio Dataset for Audio tagging. We train on 48KHz and test on 16KHz Audio.
About the Dataset
The annotations are based on a set of 7 label classes. For each chunk, multi-label annotations were first obtained for each of 3 annotators. There are 1946 such 'strong agreement' chunks is the development dataset, and 816 such 'strong agreement' chunks in the evaluation dataset.
Cloning the repo
Go ahead and clone this repository using
$ git clone https://github.com/DeepLearn-lab/audio_CHIME.git

Quick Run
If you are looking for a quick running version go inside single_file folder and run
$ python mainfile.py

Detailed Task
The process involves three steps:
  1. Feature Extraction
  2. Training on Development Dataset
  3. Testing on Evaluation Dataset
Feature Extraction
We are going to extract mel frequencies on raw audio waveforms. Go ahead and uncomment
feature_extraction function which would extract these features and save it in the .f pickle.
Training
We train our model on these extracted featuers. We use a convolution neural network for training and testing purpose. Alteration in model can be done in model.py file.
All hyper-parameters can be set in util.py. Once you have made all the required changes or want to run on the pre-set ones, run
$ python mainfile.py 
This will run the model which we test and use EER for rating our model.
About
📣 Audio tagging using deep models on CHIME-2016 dataset
Topics
audio-processingdeep-learning
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages