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Multichannel Speech Signal Multiplexing and Demultiplexing Algorithm Design

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Project Description

In the process, the source signals level, frequency content, dynamics, and panoramic position are manipulated and effects such as reverb may be added. In this project 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. We balance the modeling errors from individual sources against annorm 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.

Modules Used
Coding and Compression in Speech Processing
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