Subspace and Frequency Domain Speech Enhancement Techniques

Tejaswini, Ragipati Naga Sai; Kandagatla, Ravi Kumar; Nandeti, Jahnavi; Krupakar, Mamidi; Haveela, Paragati

doi:10.1109/rteict52294.2021.9573833

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Tan et al [14] proposed an end-to-end multi task model for VAD which increases the robustness of VAD system for low SNR conditions. Tejaswiniet al [15] compared the different approaches available both in frequency domain and time domain. They have discussed the steps involved in speech enhancement using MATLAB.…”

Section: Introductionmentioning

confidence: 99%

Short-term uncleaned signal to noise threshold ratio based end-to-end time domain speech enhancement in digital hearing aids

Nimmagadda¹,

Swamy

Prathima³

et al. 2022

IJEECS

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This paper presents the improvements in the combined solution for the noise estimation and the speech enhancement in digital hearing aids in time domain. This study focuses on the single channel statistical temporal speech enhancement using adaptive Wiener filtering. In this technique, the noise is updated based on the short-term uncleaned signal to noise threshold ratio (ST-USNTR) of the frame. It works best if and only if the back ground noise level is low compared to that of speech of interest. We considered the time domain algorithms in order to consider the time varying nature of speech signal. The performance of the proposed algorithm is evaluated for speech signal with seven ty pes of noises and three signal to noise ratios (SNR) levels in each type of noise. From the results, it is clear that the basic level of adaptive speech enhancement is obtained using statistical parameters of noisy speech without the need for reference input.

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Section: Introductionmentioning

confidence: 99%