This dataset accompanies the paper: M.Miron, J.Janer,E.Gomez,"Monaural score-informed source separation for classical music using convolutional neural networks", ISMIR 2017, http://mtg.upf.edu/node/3806 The files are based on Bach10 dataset which comprises 10 Bach chorales: http://music.cs.northwestern.edu/data/Bach10.html It comprises results in terms of SDR, SIR, SAR as .mat files for the methods presented in the paper. Additionally, we include audio .wav files for the proposed score-informed source separation method using convolutional neural networks and for the score-informed NMF counterpart. The code is available at the github repository: https://github.com/MTG/DeepConvSep/tree/master/examples/bach10_scoreinformed We include the trained CNN model for the proposed approach, which can be used to separate Bach chorales with the code provided at the github repository.