[CLASSES] DeepConvSep: Deep Convolutional Neural Networks for Musical Source Separation
List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).
List of publications acknowledging the funding in Scopus.
The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository 
as well as at the UPF e-repository 
This repository contains classes for data generation and preprocessing and feature computation, useful in training neural networks with large datasets that do not fit into memory. Additionally, you can find classes to query samples of instrument sounds from RWC instrument sound dataset.
In the 'examples' folder you can find use cases for the classes above for the case of music source separation. We provide code for feature computation (STFT) and for training convolutional neural networks for music source separation: singing voice source separation with the dataset iKala dataset, for voice, bass, drums separation with DSD100 dataset, for bassoon, clarinet, saxophone, violin with Bach10 dataset. The later is a good example for training a neural network with instrument samples from the RWC instrument sound database RWC instrument sound dataset, when the original score is available.
In the 'evaluation' folder you can find matlab code to evaluate the quality of separation, based on BSS eval.
For training neural networks we use Lasagne and Theano.
We provide code for separation using already trained models for different tasks.