Perceptually weighted multi-band spectral subtraction speech enhancement technique

Chowdhury, Faqrul A.; Alam, Jahangir; Alam, Fasiul; O’Shaughnessy, D.

doi:10.1109/icece.2008.4769239

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…Therefore the gain factor, which decides the amount of estimated noise subtracted from the noisy speech, is controlled to be lower in the formants and higher in the valleys" in 2007 [7], referred by Chowdhury, F.A. et al in their paper [8]. On the other hand, conventional spectral subtraction has two major problems: 1) it cannot trace properly the current varying in time of the noise without re-estimating the noise component on consecutive frames; 2) it causes degradation of the speech quality by oversubtracting, because the subtracting factor is used as a large constant value in order to eliminate completely the noise element.…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement based on Sequential Noise Estimation with a Masking Property

Hayashi¹,

Suguimoto²

2012

Parallel and Distributed Computing and Systems / 790: Software Engineering and Applications

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In this paper we describe a method for enhancing speech that is corrupted by additive background noise varying in time. The proposed nonlinear spectral-subtraction approach is based on sequentially updating the estimated noise per frame and adapting a masking property of the human ear. This speech enhancement method aims to decrease noise perception on the speech voice segments, based on sequential noise estimation that follows changes in the background noise. Furthermore, to enhance speech elements obtained by using the masking effect, we developed an adaptive control of a scaling function, calculated by the regression line using the noise-masking signal-tonoise ratio (NMSNR). Experimental results show that the proposed approach can efficiently remove additive noise related to various types of noise corruption.

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Section: Introductionmentioning

confidence: 99%

Speech Enhancement based on Sequential Noise Estimation with a Masking Property

Hayashi¹,

Suguimoto²

2012

Parallel and Distributed Computing and Systems / 790: Software Engineering and Applications

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“…Then, in 2009, the perceptual frequency weighting function was introduced for improving the MBSS. This method can efficiently maximize additive noise reduction while minimizing the speech distortion [24]. The MBSS method based on phase modification and magnitude compensation was introduced in 2011.…”

Section: Introductionmentioning

confidence: 99%

An Improved Perceptual MBSS Noise Reduction with an SNR-Based VAD for a Fully Operational Digital Hearing Aid

Guo

Wang

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper first reviews the state-of-the-art noise reduction methods and points out their vulnerability in noise reduction performance and speech quality, especially under the low signal-noise ratios (SNR) environments. Then this paper presents an improved perceptual multiband spectral subtraction (MBSS) noise reduction algorithm (NRA) and a novel robust voice activity detection (VAD) based on the amended sub-band SNR. The proposed SNR-based VAD can considerably increase the accuracy of discrimination between noise and speech frame. The simulation results show that the proposed NRA has better segmental SNR (segSNR) and perceptual evaluation of speech quality (PESQ) performance than other noise reduction algorithms especially under low SNR environments. In addition, a fully operational digital hearing aid chip is designed and fabricated in the 0.13 μm CMOS process based on the proposed NRA. The final chip implementation shows that the whole chip dissipates 1.3 mA at the 1.2 V operation. The acoustic test result shows that the maximum output sound pressure level (OSPL) is 114.6 dB SPL, the equivalent input noise is 5.9 dB SPL, and the total harmonic distortion is 2.5%. So the proposed digital hearing aid chip is a promising candidate for high performance hearing-aid systems.

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