Bizarre auditory RBM Results using Pylearn2

Dear /r/MachineLearning

I was hoping you guys could help me interpret some strange results I'm getting.

I am using Pylearn2 to do the training and my model is based off the dbm demo they have.

I use spectrograms of raw auditory signals. 20 pixels (100 mS) along the horizontal axis and 44 pixels along the vertical axis (frequency)

I do a couple preprocessing steps, zero mean, remove zero column, and ZCA to make my data behave a bit nicer.

Then train my RBM using a mixture of contrastive divergence and sparsity. Once the training is complete I get receptive fields that look like this.

Looking at the structure of the training data, these receptive fields don't make much sense. I was hoping some of you machine learning masters might be able to tell me what I'm doing wrong.

Thanks,

~Nivram's Brain