DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors

Reddy, Chandan K.; Gopal, Vishak; Cutler, Ross

doi:10.48550/arxiv.2010.15258

Cited by 9 publications

(19 citation statements)

References 9 publications

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“…Three metrics are utilized to evaluate the performance of our framework, namely perceptual evaluation of speech quality (PESQ) [36], extended short-time objective intelligibility (ES-TOI) [37], and DNSMOS [38]. PESQ, and ESTOI are to evaluate the objective performance of speech quality and intelligibility.…”

Section: Results and Analysismentioning

confidence: 99%

A Simultaneous Denoising and Dereverberation Framework with Target Decoupling

Li¹,

Liu²,

Luo³

et al. 2021

Preprint

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Background noise and room reverberation are regarded as two major factors to degrade the subjective speech quality. In this paper, we propose an integrated framework to address simultaneous denoising and dereverberation under complicated scenario environments. It adopts a chain optimization strategy and designs four sub-stages accordingly. In the first two stages, we decouple the multi-task learning w.r.t. complex spectrum into magnitude and phase, and only implement noise and reverberation removal in the magnitude domain. Based on the estimated priors above, we further polish the spectrum in the third stage, where both magnitude and phase information are explicitly repaired with the residual learning. Due to the data mismatch and nonlinear effect of DNNs, the residual noise often exists in the DNN-processed spectrum. To resolve the problem, we adopt a light-weight algorithm as the post-processing module to capture and suppress the residual noise in the non-active regions. In the Interspeech 2021 Deep Noise Suppression (DNS) Challenge, our submitted system ranked top-1 for the real-time track in terms of Mean Opinion Score (MOS) with ITU-T P.835 framework.

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Section: Results and Analysismentioning

confidence: 99%

A Simultaneous Denoising and Dereverberation Framework with Target Decoupling

Li¹,

Liu²,

Luo³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Two metrics are utilized to evaluate the objective performance of different systems, namely perceptual evaluation of speech quality (PESQ) [28], and extended short-time objective intelligibility (ESTOI) [29]. Besides, to evaluate the subjective quality, DNSMOS is also adopted, which is a robust nonintrusive speech quality metric and well suitable for accurate subjective rating [30].…”

Section: Results and Analysismentioning

confidence: 99%

Learning to Inference with Early Exit in the Progressive Speech Enhancement

Li¹,

Zheng²,

Zhang³

et al. 2021

2021 29th European Signal Processing Conference (EUSIPCO)

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In real scenarios, it is often necessary and significant to control the inference speed of speech enhancement systems under different conditions. To this end, we propose a stage-wise adaptive inference approach with early exit mechanism for progressive speech enhancement. Specifically, in each stage, once the spectral distance between adjacent stages lowers the empirically preset threshold, the inference will terminate and output the estimation, which can effectively accelerate the inference speed. To further improve the performance of existing speech enhancement systems, PL-CRN++ is proposed, which is an improved version over our preliminary work PL-CRN and combines stage recurrent mechanism and complex spectral mapping. Extensive experiments are conducted on the TIMIT corpus, the results demonstrate the superiority of our system over state-of-the-art baselines in terms of PESQ, ESTOI and DNSMOS. Moreover, by adjusting the threshold, we can easily control the inference efficiency while sustaining the system performance.

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“…We use the following objective metrics to evaluate speech enhancement performance: the perceptual evaluation of speech quality (PESQ) [34], short-time objective intelligibility (STOI) [35], segmental signal-to-noise ratio (SegSNR), and three mean opinion score (MOS) prediction (i.e., signal distortion evaluation (CISG), the intrusiveness of background noise (CBAK) and overall effect (COVL)) [36]. We also evaluate the subjective quality by DNS-MOS [37], which is a robust non-intrusive perceptual speech quality metric. Higher values of all metrics indicate better performance.…”

Section: Results and Analysismentioning

confidence: 99%

Joint magnitude estimation and phase recovery using Cycle-in-Cycle GAN for non-parallel speech enhancement

Yu¹,

Li²,

Wang³

et al. 2021

Preprint

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For the lack of adequate paired noisy-clean speech corpus in many real scenarios, non-parallel training is a promising task for DNNbased speech enhancement methods. However, because of the severe mismatch between input and target speech, many previous studies only focus on magnitude spectrum estimation and remain the phase unaltered, resulting in the degraded speech quality under low signal-to-noise ratio conditions. To tackle this problem, we decouple the difficult target w.r.t. original spectrum optimization into spectral magnitude and phase, and propose a novel Cycle-in-cycle generative adversarial network (dubbed CinCGAN) to jointly estimate the spectral magnitude and phase information stage by stage. In the first stage, we pretrain a magnitude CycleGAN to coarsely denoise the spectral magnitude spectrum. In the second stage, we couple the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to recover phase information and refine the spectral magnitude simultaneously. The experimental results on the VoiceBank + Demand show that the proposed approach significantly outperforms previous baselines under non-parallel training. Experiments on training the models with standard paired data also show that the proposed method can achieve remarkable performance.

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DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors

Cited by 9 publications

References 9 publications

A Simultaneous Denoising and Dereverberation Framework with Target Decoupling

A Simultaneous Denoising and Dereverberation Framework with Target Decoupling

Learning to Inference with Early Exit in the Progressive Speech Enhancement

Joint magnitude estimation and phase recovery using Cycle-in-Cycle GAN for non-parallel speech enhancement

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