Discrete Cosine Wavelet Packet Transform and Its Application in Compressed Sensing for Speech Signal

This paper concerns the compressed sensing of speech signal. Discrete cosine wavelet packet transform (DCWPT) is proposed for speech signal based on the properties of discrete cosine transform (DCT) and wavelet packet transform (WPT). Coefficients of DCWPT can be obtained by WPT from the DCT coefficients, and speech signals are sparser in DCWPT domain than in DCT domain. In order to apply this newly efficient transform into the compressed sensing for speech signal successfully, the sparse decomposition matrix of DCWPT is constructed at first. The orthogonal matching pursuit reconstruction algorithm has also be optimized according to the sparse decomposition matrix and psycho-acoustics, and a new framework of the compressed sensing for speech signal based on DCWPT is established. The conclusion that the new method is better than the traditional DCT method is made from experiment by subjective and objective indicators.

[1]  Scott T. Rickard,et al.  Comparing Measures of Sparsity , 2008, IEEE Transactions on Information Theory.

[2]  Marc Moonen,et al.  Retrieving Sparse Patterns Using a Compressed Sensing Framework: Applications to Speech Coding Based on Sparse Linear Prediction , 2010, IEEE Signal Processing Letters.

[3]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[4]  D. Donoho.  For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .

[5]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[6]  H. B. Kekre,et al.  Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks , 2011 .

[7]  Thippur V. Sreenivas,et al.  Compressive sensing for sparsely excited speech signals , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Mads Graesbll Christensen,et al.  On compressed sensing and its application to speech and audio signals , 2009, 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers.

[9]  Dai Qiong,et al.  Research on Compressed Sensing , 2011 .

[10]  T. S. Gunawan,et al.  Evaluation of sparsifying algorithms for speech signals , 2012, 2012 International Conference on Computer and Communication Engineering (ICCCE).

[11]  Chang-Jun Fu,et al.  Research on Compressed Sensing: Research on Compressed Sensing , 2011 .