On compressed sensing and its application to speech and audio signals

Authors:
Mads Græbøll Christensen;Jan Østergaard;Søren Holdt Jensen
Affiliations:
Dept. of Media Technology, Aalborg University, Denmark;Dept. of Electronic Systems, Aalborg University, Denmark;Dept. of Electronic Systems, Aalborg University, Denmark
Venue:
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Year:
2009

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Abstract

In this paper, we consider the application of compressed sensing (aka compressive sampling) to speech and audio signals. We discuss the design considerations and issues that must be addressed in doing so, and we apply compressed sensing as a pre-processor to sparse decompositions of real speech and audio signals using dictionaries composed of windowed complex sinusoids. Our results demonstrate that the principles of compressed sensing can be applied to sparse decompositions of speech and audio signals and that it offers a significant reduction of the computational complexity, but also that such signals may pose a challenge due to their non-stationary and complex nature with varying levels of sparsity.