Franck Giron

Deep neural network based instrument extraction from music

This paper deals with the extraction of an instrument from music by using a deep neural network. As prior information, we only assume to know the instrument types that are present in the mixture and, using this information, we generate the training data from a database with solo instrument performances. The neural network is built up from rectified linear units where each hidden layer has the same number of nodes as the output layer. This allows a least squares initialization of the layer weights and speeds up the training of the network considerably compared to a traditional random initialization. We give results for two mixtures, each consisting of three instruments, and evaluate the extraction performance using BSS Eval for a varying number of hidden layers.

Improving music source separation based on deep neural networks through data augmentation and network blending

This paper deals with the separation of music into individual instrument tracks which is known to be a challenging problem. We describe two different deep neural network architectures for this task, a feed-forward and a recurrent one, and show that each of them yields themselves state-of-the art results on the SiSEC DSD100 dataset. For the recurrent network, we use data augmentation during training and show that even simple separation networks are prone to overfitting if no data augmentation is used. Furthermore, we propose a blending of both neural network systems where we linearly combine their raw outputs and then perform a multi-channel Wiener filter post-processing. This blending scheme yields the best results that have been reported to-date on the SiSEC DSD100 dataset.