Abstract
This chapter presents the application of compressive sensing (CS) theory for compression of a speech signal. The speech signal is first converted into its sparse coefficients using a signal transform. Then compressed sparse measurements of the speech signal are generated using its sparse coefficients and a measurement matrix, generated using normal Gaussian distribution. The compressed speech signal is reconstructed from its compressed sparse measurements using various CS reconstruction algorithms. In this chapter, two greedy CS reconstruction algorithms – orthogonal matching pursuit (OMP) and compressive sensing matching pursuit (COSAMP) – are used for generation of a compressed speech signal.
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Thanki, R., Borisagar, K., Borra, S. (2018). Speech Compression Technique Using Compressive Sensing Theory. In: Advance Compression and Watermarking Technique for Speech Signals. SpringerBriefs in Electrical and Computer Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-319-69069-8_4
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DOI: https://doi.org/10.1007/978-3-319-69069-8_4
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Online ISBN: 978-3-319-69069-8
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