A Generative Product-of-Filters Model of Audio
Authors: Dawen Liang, Mathew D. Hoffman, Gautham Mysore
ICLR 2014

Abstract: We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of 'filters' in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces hand-designed decompositions built of basic signal processing operations with a learned decomposition based on statistical inference. This paper formulates the PoF model and derives a mean-field method for posterior inference and a variational EM algorithm to estimate the model's free parameters. We demonstrate PoF's potential for audio processing on a bandwidth expansion task, and show that PoF can serve as an effective unsupervised feature extractor for a speaker identification task.


Peer review:
[Review]:
Please ignore the 'v3' version of the paper on the arXiv; we found a bug in the new speaker identification experiment that invalidates those new results. A new version reverting to the previous results has been submitted and will be posted shortly. We sincerely apologize for this mistake.