Perceptual Evaluation of a Music Source Separation CNN Trained With Binaural and Ambisonic Audio

This research explores the idea of using different spatial audio formats for training music source separation neural networks. DeepConvSep, a library designed by Marius Miron, Pritish Chandna, Gerard Erruz, and Hector Martel, is used as a framework for testing different convolutional neural networks for source separation. A listening test is then detailed and test results are analyzed in order to perform a perceptual evaluation of the models. Conclusions are drawn regarding the effectiveness of using spatial audio formats for training source separation neural networks. Neural networks for audio seek to enable an artificial intelligence to speak and hear akin to a human.