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Weighted Sigmoid-Based Frequency-Selective Noise Filtering for Speech Denoising

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Abstract

Estimation of noise often has a major impact on the quality of enhanced signal, especially when it comes in speech enhancement applications. The non-stationary noise statistics vary with time, making decision of speech active/inactive frame is however difficult. Further, since there is no prior information of noise distribution, the estimators use the recursive averaging with a fixed smoothing coefficient ranging from 0.70 to 0.99. This fixed smoothing coefficient actually correlates the previous frames of noise statistics. Unfortunately, using fixed smoothing coefficient, the estimator treats both speech active/inactive frames equally which may cause the leakage of speech/noise power and results in loss of speech intelligibility. To address this problem and to increase the noise estimation accuracy, this paper proposes a posteriori SNR and frequency dependent adaptive smoothing coefficient. Further, this paper investigates the performance of proposed weighted sigmoid function (WSIG) noise estimator. From both objective and subjective quality assessments, it is clearly evident that the proposed noise estimator yields considerably better tracking of noise spectral variations compared to the existing state of the art methods.

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Acknowledgements

The authors would like to thank Sh. Naresh Kumar, Director, Snow & Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), Government of India, for perusing this research. Further the authors would like to thank the human participants for conducting the listening test for subjective quality assessment.

Funding

The co-author of this paper, Dr. S.Y. Low is supported by the Fundamental Research Grants Scheme (FRGS/1/2019/TK04/USMC/02/1) under the Ministry of Higher Education, Malaysia.

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Authors and Affiliations

  1. SASE, DRDO, Government of India, Delhi, India

    Neeraj Sharma

  2. National Institute of Technology, Delhi, Chandigarh, 110040, India

    Maneesh K. Singh

  3. University of Southampton, Malaysia, Gelang Patah, Malaysia

    Siow Yong Low

  4. National Institute of Technology, Hamirpur, Hamirpur, 177005, India

    Ashok Kumar

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  1. Neeraj Sharma
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  2. Maneesh K. Singh
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  3. Siow Yong Low
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  4. Ashok Kumar
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Correspondence to Neeraj Sharma.

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Sharma, N., Singh, M.K., Low, S.Y. et al. Weighted Sigmoid-Based Frequency-Selective Noise Filtering for Speech Denoising. Circuits Syst Signal Process 40, 276–295 (2021). https://doi.org/10.1007/s00034-020-01469-9

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