  The computer vision literature shows that randomly weighted neural networks
perform reasonably as feature extractors. Following this idea, we study how
non-trained (randomly weighted) convolutional neural networks perform as
feature extractors for (music) audio classification tasks. We use features
extracted from the embeddings of deep architectures as input to a classifier -
with the goal to compare classification accuracies when using different
randomly weighted architectures. By following this methodology, we run a
comprehensive evaluation of the current deep architectures for audio
classification, and provide evidence that the architectures alone are an
important piece for resolving (music) audio problems using deep neural
networks.
