Speech enhancement based on a priori signal to noise estimation

Scalart, Pascal; Filho, Jozué Vieira

doi:10.1109/icassp.1996.543199

Cited by 597 publications

(378 citation statements)

References 8 publications

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“…The first was based on a minimum mean-square error log-spectral amplitude estimator (MMSESTSA) (Ephraim and Malah, 1985) while the second one was based on an a priori signal-to-noise estimation (Wiener-Scalart) (Scalart and Filho, 1996). Both of them were implemented by Zavarehei (2005b;2005a).…”

Section: Algorithmsmentioning

confidence: 99%

Speech intelligibility improvement using convolutive blind source separation assisted by denoising algorithms

Kociński

2008

Speech Communication

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Section: Algorithmsmentioning

confidence: 99%

Speech intelligibility improvement using convolutive blind source separation assisted by denoising algorithms

Kociński

2008

Speech Communication

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“…Two channel Wiener filtering based on a-priori SNR estimation is implemented [12] for its capability of reducing "musical noise" [3]. Its gain function is…”

Section: Y K L and ( )mentioning

confidence: 99%

“…Beamforming [6,7], post-filtering [4,5] and multi-channel Wiener filtering [3] are often used in multi-channel noise reduction. However, beamforming [6,7] requires a robust estimation of the direction of the target speech.…”

Section: Two Channel Wiener Filtermentioning

confidence: 99%

“…Traditional speech enhancement algorithms often work only in narrowly specified conditions or with specific noise statistics. Algorithms exist and work well, when the background noise is stationary and non-speech [2][3][4][5], however, these algorithms often fail when competing speakers are present. A possible solution to this problem is to use source separation algorithm like beamform-ing [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Speech Enhancement via Combination of Wiener Filter and Blind Source Separation

Taghia

Sang

et al. 2011

Advances in Intelligent and Soft Computing

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Abstract. Automatic speech recognition (ASR) often fails in acoustically noisy environments. Aimed to improve speech recognition scores of an ASR in a real-life like acoustical environment, a speech pre-processing system is proposed in this paper, which consists of several stages: First, a convolutive blind source separation (BSS) is applied to the spectrogram of the signals that are pre-processed by binaural Wiener filtering (BWF). Secondly, the target speech is detected by an ASR system recognition rate based on a Hidden Markov Model (HMM). To evaluate the performance of the proposed algorithm, the signal-to-interference ratio (SIR), the improvement signal-to-noise ratio (ISNR) and the speech recognition rates of the output signals were calculated using the signal corpus of the CHiME database. The results show an improvement in SIR and ISNR, but no obvious improvement of speech recognition scores. Improvements for future research are suggested.

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“…Several methods for reducing the influence of noise have been proposed. Among them, it is worth mentioning the Wiener filtering technique [1] and Spectral Subtraction (SS) [2], which consists of subtracting an estimate of the noise spectrum from the noisy speech spectrum. Both of them produce a more intelligible signal but generate the so called musical noise as a side effect.…”

Section: Introductionmentioning

confidence: 99%

Speech Denoising Using Non-negative Matrix Factorization with Kullback-Leibler Divergence and Sparseness Constraints

Ludeña-Choez

Gallardo-Antolín

2012

Advances in Speech and Language Technologies for Iberian Languages

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Abstract.A speech denoising method based on Non-Negative Matrix Factorization (NMF) is presented in this paper. With respect to previous related works, this paper makes two contributions. First, our method does not assume a priori knowledge about the nature of the noise. Second, it combines the use of the Kullback-Leibler divergence with sparseness constraints on the activation matrix, improving the performance of similar techniques that minimize the Euclidean distance and/or do not consider any sparsification. We evaluate the proposed method for both, speech enhancement and automatic speech recognitions tasks, and compare it to conventional spectral subtraction, showing improvements in speech quality and recognition accuracy, respectively, for different noisy conditions.

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Speech enhancement based on a priori signal to noise estimation

Cited by 597 publications

References 8 publications

Speech intelligibility improvement using convolutive blind source separation assisted by denoising algorithms

Speech intelligibility improvement using convolutive blind source separation assisted by denoising algorithms

Speech Enhancement via Combination of Wiener Filter and Blind Source Separation

Speech Denoising Using Non-negative Matrix Factorization with Kullback-Leibler Divergence and Sparseness Constraints

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