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audio_DCASE

Paper Implementation for :

[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]

This code runs on the DCASE 2016 Audio Dataset.

You need to define:

wav_dev_fd development audio folder

wav_eva_fd evaluation audio folder

dev_fd development features folder

eva_fd evaluation features folder

label_csv development meta file

txt_eva_path evaluation test file

new_p evaluation evaluate file

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.

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Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset

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GitHub - DeepLearn-lab/audio_DCASE: Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset · GitHub
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audio_DCASE
Paper Implementation for :
[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]
This code runs on the DCASE 2016 Audio Dataset.
You need to define:
wav_dev_fd development audio folder
wav_eva_fd evaluation audio folder
dev_fd development features folder
eva_fd evaluation features folder
label_csv development meta file
txt_eva_path evaluation test file
new_p evaluation evaluate file
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.
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GitHub - DeepLearn-lab/audio_DCASE: Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset · GitHub
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util.py
util.py
Repository files navigation
audio_DCASE
Paper Implementation for :
[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]
This code runs on the DCASE 2016 Audio Dataset.
You need to define:
wav_dev_fd development audio folder
wav_eva_fd evaluation audio folder
dev_fd development features folder
eva_fd evaluation features folder
label_csv development meta file
txt_eva_path evaluation test file
new_p evaluation evaluate file
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
Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset
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2 watching
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - DeepLearn-lab/audio_DCASE: Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset · GitHub
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util.py
util.py
Repository files navigation
audio_DCASE
Paper Implementation for :
[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]
This code runs on the DCASE 2016 Audio Dataset.
You need to define:
wav_dev_fd development audio folder
wav_eva_fd evaluation audio folder
dev_fd development features folder
eva_fd evaluation features folder
label_csv development meta file
txt_eva_path evaluation test file
new_p evaluation evaluate file
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
Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset
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'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
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var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
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}
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document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
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cleanLinks();
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
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observer.observe(document.body, { childList: true, subtree: true });
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} 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_DCASE: Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset · GitHub
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mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
audio_DCASE
Paper Implementation for :
[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]
This code runs on the DCASE 2016 Audio Dataset.
You need to define:
wav_dev_fd development audio folder
wav_eva_fd evaluation audio folder
dev_fd development features folder
eva_fd evaluation features folder
label_csv development meta file
txt_eva_path evaluation test file
new_p evaluation evaluate file
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
Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset
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Watchers
2 watching
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, 'i');
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GitHub - DeepLearn-lab/audio_DCASE: Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset · GitHub
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mainfile.py
model.py
model.py
util.py
util.py
Repository files navigation
audio_DCASE
Paper Implementation for :
[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]
This code runs on the DCASE 2016 Audio Dataset.
You need to define:
wav_dev_fd development audio folder
wav_eva_fd evaluation audio folder
dev_fd development features folder
eva_fd evaluation features folder
label_csv development meta file
txt_eva_path evaluation test file
new_p evaluation evaluate file
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
Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset
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2 watching
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(function(){
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GitHub - DeepLearn-lab/audio_DCASE: Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset · GitHub
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11 Commits
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model.py
util.py
util.py
Repository files navigation
audio_DCASE
Paper Implementation for :
[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]
This code runs on the DCASE 2016 Audio Dataset.
You need to define:
wav_dev_fd development audio folder
wav_eva_fd evaluation audio folder
dev_fd development features folder
eva_fd evaluation features folder
label_csv development meta file
txt_eva_path evaluation test file
new_p evaluation evaluate file
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
Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset
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Readme
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Stars
2 stars
Watchers
2 watching
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, 'i');
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// Universal Dark Mode - works on any site
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var enabled = true;
function applyDarkMode() {
if (!enabled) return;
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filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
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var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
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e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
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applyDarkMode();
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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); }
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GitHub - DeepLearn-lab/audio_DCASE: Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset · GitHub
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util.py
Repository files navigation
audio_DCASE
Paper Implementation for :
[1] Deep Neural Network Baseline For Dcase Challenge 2016 [Paper]
This code runs on the DCASE 2016 Audio Dataset.
You need to define:
wav_dev_fd development audio folder
wav_eva_fd evaluation audio folder
dev_fd development features folder
eva_fd evaluation features folder
label_csv development meta file
txt_eva_path evaluation test file
new_p evaluation evaluate file
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
Acoustic Scene Recognition using deep neural models on DCASE 2016 dataset
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Activity
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