Examining the Mapping Functions of Denoising Autoencoders in Music Source Separation
| Main Authors: | Stylianos Ioannis Mimilakis, Konstantinos Drossos, Estefania Cano, Gerald Schuller |
|---|---|
| Format: | info Lainnya Journal |
| Bahasa: | eng |
| Terbitan: |
, 2019
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/2629650 |
| ctrlnum |
2629650 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Stylianos Ioannis Mimilakis</creator><creator>Konstantinos Drossos</creator><creator>Estefania Cano</creator><creator>Gerald Schuller</creator><date>2019-04-05</date><description>Support material for the article: "Examining the Mapping Functions of Denoising Autoencoders in Music Source Separation".
It contains the code for:
Analyzing an open-access dataset
Implementations of the Denoising Autoencoder and the variants for music source separation
Loss functions employed in the article
The algorithm that is denoted as the Neural Couplings Algorithm (NCA)
Reproducing the figures of the corresponding article
Toy-examples for the gradient calculation
It also contains binary files used in:
Creating the segments for the testing of the NCA and creating the corresponding visualizations
The optimized models' weights
Python requirements text file
How to use:
Install the requirements
Set the data-set paths in "processes_scripts/run_me.py" and "processes_scripts/analyze_data.py"
Run "process_scripts/analyze_data.py" to analyze and store the spectral data for training the models
Run "process_scripts/run_me" to train (in the case that re-training is necessary with "training_flag = True") and to compute the linearly composed matrices for reproducing one of the figures
Run "process_scripts/run_couplings.py" to plot the average linear composition, evaluate the strategies of the NCA algorithm and make plots on specific test data.
TBD:
Github gist for the NCA algorithm
Pytorch and python updates
</description><identifier>https://zenodo.org/record/2629650</identifier><identifier>10.5281/zenodo.2629650</identifier><identifier>oai:zenodo.org:2629650</identifier><language>eng</language><relation>info:eu-repo/grantAgreement/EC/H2020/642685/</relation><relation>doi:10.5281/zenodo.2629649</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Deep learning</subject><subject>Music Source Separation</subject><subject>Denoising Autoencoders</subject><subject>Neural Couplings Algorithm</subject><title>Examining the Mapping Functions of Denoising Autoencoders in Music Source Separation</title><type>Other:info:eu-repo/semantics/other</type><type>Other:Other</type><recordID>2629650</recordID></dc>
|
| language |
eng |
| format |
Other:info:eu-repo/semantics/other Other Other:Other Journal:Journal Journal |
| author |
Stylianos Ioannis Mimilakis Konstantinos Drossos Estefania Cano Gerald Schuller |
| title |
Examining the Mapping Functions of Denoising Autoencoders in Music Source Separation |
| publishDate |
2019 |
| topic |
Deep learning Music Source Separation Denoising Autoencoders Neural Couplings Algorithm |
| url |
https://zenodo.org/record/2629650 |
| contents |
Support material for the article: "Examining the Mapping Functions of Denoising Autoencoders in Music Source Separation".
It contains the code for:
Analyzing an open-access dataset
Implementations of the Denoising Autoencoder and the variants for music source separation
Loss functions employed in the article
The algorithm that is denoted as the Neural Couplings Algorithm (NCA)
Reproducing the figures of the corresponding article
Toy-examples for the gradient calculation
It also contains binary files used in:
Creating the segments for the testing of the NCA and creating the corresponding visualizations
The optimized models' weights
Python requirements text file
How to use:
Install the requirements
Set the data-set paths in "processes_scripts/run_me.py" and "processes_scripts/analyze_data.py"
Run "process_scripts/analyze_data.py" to analyze and store the spectral data for training the models
Run "process_scripts/run_me" to train (in the case that re-training is necessary with "training_flag = True") and to compute the linearly composed matrices for reproducing one of the figures
Run "process_scripts/run_couplings.py" to plot the average linear composition, evaluate the strategies of the NCA algorithm and make plots on specific test data.
TBD:
Github gist for the NCA algorithm
Pytorch and python updates
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ZAIN Publications |
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7213 |
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Cognizance Journal of Multidisciplinary Studies |
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5267 |
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Cognizance Journal of Multidisciplinary Studies |
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Multidisciplinary |
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Stockholm |
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INTERNASIONAL |
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1 |
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1734903500881526784 |
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17.608969 |