  Several results in the computer vision literature have shown the potential of
randomly weighted neural networks. While they perform fairly well as feature
extractors for discriminative tasks, a positive correlation exists between
their performance and their fully trained counterparts. According to these
discoveries, we pose two questions: what is the value of randomly weighted
networks in difficult generative audio tasks such as audio source separation
and does such positive correlation still exist when it comes to large random
networks and their trained counterparts? In this paper, we demonstrate that the
positive correlation still exists. Based on this discovery, we can try out
different architecture designs or tricks without training the whole model.
Meanwhile, we find a surprising result that in comparison to the non-trained
encoder (down-sample path) in Wave-U-Net, fixing the decoder (up-sample path)
to random weights results in better performance, almost comparable to the fully
trained model.
