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
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/2629650 |
Daftar Isi:
- 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