Title :
Sparse decomposition of mixed audio signals by basis pursuit with autoregressive models
Author :
Cho, Youngmin ; Saul, Lawrence K.
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of California, San Diego, CA
Abstract :
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 lscr1-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 sources. We derive the required optimizations in this framework and present experimental results on combinations of periodic and aperiodic sources.
Keywords :
acoustic signal processing; audio acoustics; audio signal processing; autoregressive processes; optimisation; regression analysis; acoustic waveform; autoregressive model; grouped variable; linear combination; mixed audio signal; optimization; regularized linear regression analysis; sparse decomposition; static template; Acoustic waves; Audio recording; Dictionaries; Humans; Machine learning; Microphones; Psychoacoustic models; Software libraries; Source separation; Vectors; Machine learning; Signal detection; Sparsity;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2009.4959931