Simultaneous speech coding and de-noising in a dictionary based quantized CS framework

Abstract

Speech compression or speech coding is inevitable for effective communication of speech signals in resource limited scenarios and researcher's have been working on achieving lower and lower transmission bit rates (BR) without much compromise on the quality of speech. Medium BR hybrid speech coding schemes have gained much interest in the recent years with most of them based on CELP, the basic medium bit-rate coding scheme. In this work, we provide an insight to the capabilities of compressive sensing (CS) in speech processing and propose a novel idea in the quantized framework. Three major aspects demonstrated in this paper are (1) Inherent denoising of noisy speech by the CS based coder along with compression (2) Quantization of CS measurements to achieve medium transmission bit-rates and (3) Enhancement of quality and compression performance of the coder with better sparse representations of speech using dictionaries. The results indicate that the proposed scheme offers better compression in comparison with basic Gaussian codebook CELP. The CS scheme has the added advantage of inherent noise suppression and provides more robustness to background noise in comparison with parameter extraction based medium bit-rate speech coding systems.

Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment*

“…In recent years, many researchers have conducted denoising studies on nonlinear time series. Inspired by Donoho, [11] Chen et al, [12] and Ramdas et al, [13] the denoising method of HIFU echo signals based on compressed sensing (CS) is proposed. CS can transform the signal from the time domain to the sparse domain and then reconstruct the signal, which can retain the useful information of the signal and remove the noise.…”