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A Novel Compressed Sensing Approach to Speech Signal Compression

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 371)

Abstract

Compressed sensing (CS) is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper, we apply the iterative hard thresholding (IHT) algorithm for compressed sensing on the speech signal. The interested speech signal is transformed to the frequency domain using Discrete Fourier Transform (DCT) and then compressed sensing is applied to that signal. The compressed signal can be reconstructed using the recently introduced Iterative Hard Thresholding (IHT) algorithm and also by the tradditional \( \ell_{1} \) minimization (basic pursuit) for comparison. It is shown that the compressed sensing can provide better root mean square error (RMSE) than the tradition DCT compression method, given the same compression ratio.

Keywords

  • Compressed sensing
  • Iterative hard thresholding
  • Speech signal compression
  • DCT
  • \( \ell_{1} \) minimization

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Correspondence to Phuong T. Tran .

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Nguyen, T.N., Tran, P.T., Voznak, M. (2016). A Novel Compressed Sensing Approach to Speech Signal Compression. In: Duy, V., Dao, T., Zelinka, I., Choi, HS., Chadli, M. (eds) AETA 2015: Recent Advances in Electrical Engineering and Related Sciences. Lecture Notes in Electrical Engineering, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-319-27247-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-27247-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27245-0

  • Online ISBN: 978-3-319-27247-4

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