DeepConvSep | Deep Convolutional Neural Networks for Musical Source | Machine Learning library

DeepConvSep is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Neural Network applications. DeepConvSep has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. However DeepConvSep has 5 bugs. You can download it from GitHub.

Deep Convolutional Neural Networks for Musical Source Separation. 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.