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

J-Net: Randomly weighted U-Net for audio source separation