Sparse decomposition of mixed audio signals by basis pursuit with autoregressive models

We develop a framework to detect when certain sounds are present in a mixed audio signal. We focus on the regime where out of a large number of possible sounds, a small but unknown number are combined and overlapped to yield the observed signal. To infer which sounds are present, we attempt to decompose the observed signal as a linear combination of a small number of sources. To encourage sparse solutions with this property, we balance the modeling errors from individual sources against an 1-norm penalty of the type used in basis pursuit and regularized linear regression with grouped variables. Our approach can be viewed as a novel generalization of basis pursuit in two ways: first, with a dictionary of fixed size, we attempt to model acoustic waveforms of potentially variable duration; second, for dictionary entries, we do not store basis vectors representing static templates, but the coefficients of autoregressive models that characterize the acoustic variability of individual so...

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Youngmin Cho, Lawrence K. Saul
• Keywords: Basis Pursuit, ICASSP 2009, Individual Sources, Observed Signal, Signal Processing