Sreenivas, TV and Kleijn, W Bastiaan (2009) Compressive Sensing For Sparsely Excited Speech Signals. In: IEEE International Conference on Acoustics, Speech and Signal Processing, APR 19-24, 2009, Taipei, Taiwan, pp. 4125-4128.
Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived from the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Keywords:||sampling; compressed sensing; matching pursuit; sparse signal reconstruction.|
|Department/Centre:||Division of Electrical Sciences > Electrical Communication Engineering|
|Date Deposited:||08 Sep 2009 09:39|
|Last Modified:||19 Sep 2010 05:44|
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