Noise estimation with lowcomplexity for speech enhancement

Yong, Pei Chee; Nordholm, Sven; Dam, Hai Huyen

doi:10.1109/aspaa.2011.6082280

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In this work, the noise PSD is estimated by using the step-size controlled noise estimator in [6]. The parameter β denotes the smoothing factor and P [.]…”

Section: Traditional Decision-directed Approachmentioning

confidence: 99%

Trade-off evaluation for speech enhancement algorithms with respect to the a priori SNR estimation

Yong

Nordholm

Dam

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, a modified a priori SNR estimator is proposed for speech enhancement. The well-known decision-directed (DD) approach is modified by matching each gain function with the noisy speech spectrum at current frame rather than the previous one. The proposed algorithm eliminates the speech transient distortion and reduces the impact from the choice of the gain function towards the level of smoothing in the SNR estimate. An objective evaluation metric is employed to measure the trade-off between musical noise, noise reduction and speech distortion. Performance is evaluated and compared between a modified sigmoid gain function, the state-ofthe-art log-spectral amplitude estimator and the Wiener filter. Simulation results show that the modified DD approach performs better in terms of the trade-off evaluation.

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“…In this work, the noise PSD is estimated by using the step-size controlled noise estimator in [6]. The parameter β denotes the smoothing factor and P [.]…”

Section: Traditional Decision-directed Approachmentioning

confidence: 99%

Trade-off evaluation for speech enhancement algorithms with respect to the a priori SNR estimation

Yong

Nordholm

Dam

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

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“…Many studies have been conducted to estimate noise [1][2][3][4][5][6][7][8][9][10][11]. Kianyfar and Abutalebi [1] proposed a noise estimator, which employed speech presence probability to update noise variance.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties

Lei

Shen

et al. 2016

Applied Sciences

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Abstract:The accuracy of noise estimation is important for the performance of a speech denoising system. Most noise estimators suffer from either overestimation or underestimation on the noise level. An overestimate on noise magnitude will cause serious speech distortion for speech denoising. Conversely, a great quantity of residual noise will occur when the noise magnitude is underestimated. Accurately estimating noise magnitude is important for speech denoising. This study proposes employing variable segment length for noise tracking and variable thresholds for the determination of speech presence probability, resulting in the performance improvement for a minima-controlled-recursive-averaging (MCRA) algorithm in noise estimation. Initially, the fundamental frequency was estimated to determine whether a frame is a vowel. In the case of a vowel frame, the increment of segment lengths and the decrement of threshold for speech presence were performed which resulted in underestimating the level of noise magnitude. Accordingly, the speech distortion is reduced in denoised speech. On the contrary, the segment length decreases rapidly in noise-dominant regions. This enables the noise estimate to update quickly and the noise variation to track well, yielding interference noise being removed effectively through the process of speech denoising. Experimental results show that the proposed approach has been effective in improving the performance of the MCRA algorithm by preserving the weak vowels and consonants. The denoising performance is therefore improved.

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Noise Estimation for Speech Enhancement Using Minimum-Spectral-Average and Vowel-Presence Detection Approach

Chen

Shen

Wang

et al. 2017

Lecture Notes in Electrical Engineering

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Noise estimation with lowcomplexity for speech enhancement

Cited by 5 publications

References 9 publications

Trade-off evaluation for speech enhancement algorithms with respect to the a priori SNR estimation

Trade-off evaluation for speech enhancement algorithms with respect to the a priori SNR estimation

Estimation of Noise Magnitude for Speech Denoising Using Minima-Controlled-Recursive-Averaging Algorithm Adapted by Harmonic Properties

Noise Estimation for Speech Enhancement Using Minimum-Spectral-Average and Vowel-Presence Detection Approach

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