Enhancement of speech corrupted by acoustic noise

Berouti, M.; Schwartz, Robert N.; Makhoul, John

doi:10.1109/icassp.1979.1170788

Cited by 982 publications

(708 citation statements)

References 2 publications

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“…Note that when the signal is surely absent (i.e., whenp tk ¼ 0), the resulting estimatorX tk reduces to a constant attenuation of Y tk (i.e.,X tk ¼ G min Y tk ). This retains the noise naturalness, and is closely related to the ''spectral floor'' proposed by Berouti et al [23].…”

Section: Article In Pressmentioning

confidence: 99%

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Cohen

2006

Signal Processing

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In this paper, we develop and evaluate speech enhancement algorithms, which are based on supergaussian generalized autoregressive conditional heteroscedasticity (GARCH) models in the short-time Fourier transform (STFT) domain. We consider three different statistical models, two fidelity criteria, and two approaches for the estimation of the variances of the STFT coefficients. The statistical model is either Gaussian, Gamma or Laplacian; the fidelity criteria include minimum mean-squared error (MMSE) of the STFT coefficients and MMSE of the log-spectral amplitude (LSA); the spectral variance is estimated based on either the proposed GARCH models or the decision-directed method of Ephraim and Malah. We show that estimating the variance by the GARCH modeling method yields lower log-spectral distortion and higher perceptual evaluation of speech quality scores (PESQ, ITU-T P.862) than by using the decisiondirected method, whether the presumed statistical model is Gaussian, Gamma or Laplacian, and whether the fidelity criterion is MMSE of the STFT coefficients or MMSE of the LSA. furthermore while a gaussian model is inferior to the supergaussian models when USING the decision-directed method, the Gaussian model is superior when using the garch modeling method. r 2005 Published by Elsevier B.V.

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Section: Article In Pressmentioning

confidence: 99%

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Cohen

2006

Signal Processing

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“…Classical single-channel noise reduction methods include variants of spectral subtraction [2,3], where an averaged noise (magnitude or power) spectrum is subtracted from the noisy signal spectrum while keeping the resultant spectral magnitudes positive, and Wiener filtering [16], often implemented in practice using an iterative approach [12].…”

Section: Previous Workmentioning

confidence: 99%

Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition

Pohjalainen

Ringeval

Schuller

2016

Proceedings of the 24th ACM International Conference on Multimedia

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Signal noise reduction can improve the performance of machine learning systems dealing with time signals such as audio. Real-life applicability of these recognition technologies requires the system to uphold its performance level in variable, challenging conditions such as noisy environments. In this contribution, we investigate audio signal denoising methods in cepstral and log-spectral domains and compare them with common implementations of standard techniques. The di↵erent approaches are first compared generally using averaged acoustic distance metrics. They are then applied to automatic recognition of spontaneous and natural emotions under simulated smartphone-recorded noisy conditions. Emotion recognition is implemented as support vector regression for continuous-valued prediction of arousal and valence on a realistic multimodal database. In the experiments, the proposed methods are found to generally outperform standard noise reduction algorithms.

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“…This method is capable of eliminating the stationary noises. Then, this method was developed by Berouti in order to improve the spectral subtraction to avoid musical noises [16]. In 1994, Martin [17] developed an algorithm which eliminates the need for explicit speech pause detection without substantial increase in computational complexity based on spectral subtraction and minimum statistics.…”

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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Enhancement of speech corrupted by acoustic noise

Cited by 982 publications

References 2 publications

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models

Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition

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

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