Convex combination framework for a priori SNR estimation in speech enhancement

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

doi:10.1109/icassp.2017.7953103

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Tables 11 and 12 demonstrate the average results of the listening test in terms of speech quality, background noise, musical noise, and the overall performance of each estimation method by determining the mean of the rating scores. Ten normal hearing participants in the age of (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) took part in this test. They were asked to rate speech signals estimated by three different a priori SNR estimators in terms of speech, background noise, and musical noise as explained in the previous section.…”

Section: Evaluation Of Listening Testsmentioning

confidence: 99%

“…In this paper, we extend the research in [24], which includes an improved a priori SNR estimation based on modeling the speech absence probability with a sigmoid function. This sigmoid function was used to control the adaptation speed of the a priori SNR estimation.…”

Section: Introductionmentioning

confidence: 97%

“…Previously, only the Wiener gain function was considered. In this work, we propose an improved a priori SNR estimation [24] using different gain functions, namely, Wiener filter (WF) [25] and MMSE-LSA gain function [17]. A new evaluation technique referred to as the modified Hamming distance (HD) has also been proposed.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive a priori SNR estimator for perceptual speech enhancement

Nahma

Yong²,

Dam

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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In this paper, an adaptive averaging a priori SNR estimation employing critical band processing is proposed. The proposed method modifies the current decision-directed a priori SNR estimation to achieve faster tracking when SNR changes. The decision-directed estimator (DD) employs a fixed weighting with the value close to one, which makes it slow in following the onsets of speech utterances. The proposed SNR estimator provides a means to solve this issue by employing an adaptive weighting factor. This allows an improved tracking of onset changes in the speech signal. As a consequence, it results in better preservation of speech components. This adaptive technique ensures that the weighting between the modified decision-directed a priori estimate and the maximum likelihood a priori estimate is a function of the speech absence probability. The estimate of the speech absence probability is modeled by a sigmoid function. Furthermore, a critical band mapping for the short-time Fourier transform analysis-synthesis system is utilized in the speech enhancement to achieve less musical noise. In addition, to evaluate the ability of the a priori SNR estimation method in preserving speech components, we proposed a modified objective measurement known as modified hamming distance. Evaluations are performed by utilizing both objective and subjective measurements. The experimental results show that the proposed method improves the speech quality under different noise conditions. Moreover, it maintains the advantage of the DD approach in eliminating the musical noise under different SNR conditions. The objective results are supported by subjective listening tests using 10 subjects (5 males and 5 females).

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Section: Evaluation Of Listening Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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An adaptive a priori SNR estimator for perceptual speech enhancement

Nahma

Yong²,

Dam

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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Cross Evaluation of Speech Enhancement Methods under Different Noise Conditions

Nahma

Yong

Dam

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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DNN-Based Cepstral Excitation Manipulation for Speech Enhancement

Elshamy

Fingscheidt

2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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This contribution aims at speech model-based speech enhancement by exploiting the source-filter model of human speech production. The proposed method enhances the excitation signal in the cepstral domain by making use of a deep neural network (DNN). We investigate two types of target representations along with the significant effects of their normalization. The new approach exceeds the performance of a formerly introduced classical signal processing-based cepstral excitation manipulation (CEM) method in terms of noise attenuation by about 1.5 dB. We show that this gain also holds true when comparing serial combinations of envelope and excitation enhancement. In the important low-SNR conditions, no significant trade-off for speech component quality or speech intelligibility is induced, while allowing for substantially higher noise attenuation. In total, a traditional purely statistical state-of-the-art speech enhancement system is outperformed by more than 3 dB noise attenuation.

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Convex combination framework for a priori SNR estimation in speech enhancement

Cited by 4 publications

References 17 publications

An adaptive a priori SNR estimator for perceptual speech enhancement

An adaptive a priori SNR estimator for perceptual speech enhancement

Cross Evaluation of Speech Enhancement Methods under Different Noise Conditions

DNN-Based Cepstral Excitation Manipulation for Speech Enhancement

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