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International Journal of Speech Technology

Erschienen in:

08.08.2019

Performance comparison of sparsifying basis functions for compressive speech enhancement

verfasst von: Smriti Sahu, Neela Rayavarapu

Erschienen in: International Journal of Speech Technology | Ausgabe 3/2019

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Abstract

Speech enhancement using Compressive Sensing algorithm is a different paradigm from the conventional enhancement and compression techniques. Compressive Sensing requires very less number of samples compared to the Nyquist sampling for the purpose of reconstructing the signal. This work provides a comparative analysis of three sparsifying basis functions DFT, DCT and DWT for compressive speech enhancement on the basis of five objective measures. The objective measures used are: Signal to Noise Ratio, Segmental Signal to Noise Ratio, Log-Likelihood Ratio, Perceptual Evaluation of Speech Quality and processing time. Experimental analysis concludes that DWT as a basis function performs better than the others for compressive speech enhancement. As orthogonal wavelets are suitable for signal denoising, this work further investigates the performance of the four orthogonal wavelet families: Daubechies, Coiflets, Symlets and Fejér-Korovkin as basis functions for the purpose of compressive speech enhancement.

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Titel: Performance comparison of sparsifying basis functions for compressive speech enhancement
verfasst von: Smriti Sahu
Neela Rayavarapu
Publikationsdatum: 08.08.2019
Verlag: Springer US
Erschienen in: International Journal of Speech Technology / Ausgabe 3/2019
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI: https://doi.org/10.1007/s10772-019-09622-9

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