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中文摘要：
Based on the approximate sparseness of speech in wavelet basis,a compressed sensing theory is applied to compress and reconstruct speech signals.Compared with one-dimensional orthogonal wavelet transform(OWT),two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing,furthermore,the threshold is set to improve the sparseness.Then an adaptive subgradient projection method(ASPM)is adopted for speech reconstruction in compressed sensing.Meanwhile,mechanism which adaptively adjusts inflation parameter in different iterations has been designed for fast convergence.Theoretical analysis and simulation results conclude that this algorithm has fast convergence,and lower reconstruction error,and also exhibits higher robustness in different noise intensities.
英文摘要：
Based on the approximate sparseness of speech in wavelet basis, a compressed sensing theory is applied to compress and reconstruct speech signals. Compared with one-dimensional orthogonal wavelet transform （OWT）, two-dimensional OWT combined with Dmeyer and biorthogonal wavelet is firstly proposed to raise running efficiency in speech frame processing, furthermore, the threshold is set to improve the sparseness. Then an adaptive subgradient projection method （ASPM） is adopt- ed for speech reconstruction in compressed sensing. Meanwhile, mechanism which adaptively ad- justs inflation parameter in different iterations has been designed for fast convergence. Theoretical analysis and simulation results conclude that this algorithm has fast convergence, and lower reconstruction error, and also exhibits higher robustness in different noise intensities.
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