In "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations" by Lee et. al.(PDF) Convolutional DBN's are proposed. Also the method is evaluated for image classification. This sounds logical, as there are natural local image features, like small corners and edges etc.
In "Unsupervised feature learning for audio classification using convolutional deep belief networks" by Lee et. al. this method is applied for audio in different types of classifications. Speaker identification, gender indentification, phone classification and also some music genre / artist classification.
How can the convolutional part of this network be interpreted for audio, like it can be explained for images as edges?