Iclr 2018 C Onvolutional Vs . R Ecurrent N Eural N Et - Works for a Udio S Ource S Eparation

We propose a convolutional neural network as an alternative to recurrent neural networks for separating out individual speakers in a sound mixture. Our results achieve state-of-the-art results with an order of magnitude fewer parameters. We also characterize the robustness of both models to generalize to three different testing conditions including a novel dataset. We create a new dataset RealTalkLibri which evaluates how well source separation models generalize to real world mixtures. Our results indicate the acoustics of the environment have significant impact on the performance of all neural network models, with the convolutional model showing superior ability to generalize to new environments.