Enhancement and bandwidth compression of noisy speech

Lim, Jae S.; Oppenheim, A.

doi:10.1109/proc.1979.11540

Cited by 956 publications

(445 citation statements)

References 38 publications

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“…GSS first obtains the magnitudes of the SSTFT of the enhanced speech jŜ r [k]j's, which we shall refer hereafter as spectral magnitudes, and the phases arg(Ŝr[k])'s with the following recipe: The parameter has some effect on speech intelligibility, whereas controls the amount of noise suppression. For example, when is set to two, the enhanced speech often appears to be more intelligible than that obtained with an equals to 1 or 0.5 (see [1]). However, musical noise seems to be relatively more annoying for the case where = 2.…”

Section: Preliminary Discussion and Motivationmentioning

confidence: 99%

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Postprocessing method for suppressing musical noise generated by spectral subtraction

Goh

Tan

1998

IEEE Trans. Speech Audio Process.

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Section: Preliminary Discussion and Motivationmentioning

confidence: 99%

“…In the literature, a variety of speech enhancement methods capable of suppressing noise has been proposed. Spectral subtraction [1], [2] is among the traditional methods that have been extensively studied.…”

Section: Introductionmentioning

confidence: 99%

Postprocessing method for suppressing musical noise generated by spectral subtraction

Goh

Tan

1998

IEEE Trans. Speech Audio Process.

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“…For example, A (n bits) can be split as shown in Eq. (19), where a (n−1) ∼ a (0) is 0 or 1. So the multiplication of two operands, A and B, can be expressed as the sum-shift format, as shown in Eq.…”

Section: Low Computational Complexity Considerationmentioning

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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“…To evaluate our method, we implement and compare our method with the standard spectral subtraction method [9] and Wiener-filter based method [10], in which all of them are non-learning based methods. We also compare our method with the standard spectral conversion method [7] applying for noisy speech enhancement.…”

Section: Objective Evaluations For Voice Quality Of Enhanced Noisymentioning

confidence: 99%

A Novel Spectral Conversion Based Approach for Noisy Speech Enhancement

Do¹

2011

IJIEE

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Abstract-Present noisy speech enhancements algorithms are efficiently used for additive noise but not very good for convolutive noise as reverberation. And even for additive noise, the estimation of noise, when only one microphone source is provided, is based on the assumption of a slowly varying noise environment, commonly assumed as stationary noise. However, real noise is non-stationary noise, which difficult to be efficiently estimated. Spectral conversion can be used for predicting the vocal tract (spectral envelope) parameters of noisy speech without estimating the parameters of the noise source. Therefore, it can be applied to a general speech enhancement model, for both stationary and non-stationary additive noise environment, as well as convolutive noise environment, when only one microphone source is provided. In this paper, we propose a spectral conversion based speech enhancement method. The experimental results show that our method outperforms traditional methods.

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Enhancement and bandwidth compression of noisy speech

Cited by 956 publications

References 38 publications

Postprocessing method for suppressing musical noise generated by spectral subtraction

Postprocessing method for suppressing musical noise generated by spectral subtraction

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

A Novel Spectral Conversion Based Approach for Noisy Speech Enhancement

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