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Single Channel Speech Enhancement Using Masking Based on Sinusoidal Modeling

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1576))

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Abstract

This paper focused on development of single channel speech enhancement method. Conventional noise reduction methods based on filtering like Wiener filtering and masking uses spectral magnitudes. These magnitudes are obtained from time-frequency representation of noisy speech signals. Here, speech signal is analyzed using sinusoidal modelling. Filter gain is developed for masking of the background noise based on sinusoidal components. The developed system’s performance is evaluated using Perceptual Evaluation of Speech Quality (PESQ). It is evident from experiments that proposed approach displaying better performance compared to existing approaches.

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Acknowledgement

This work is supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India and, file no. is EEQ/2018/001338, dated 27th February 2019.

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Authors and Affiliations

  1. National Institute of Technology Meghalaya, Shillong, 793003, India

    Rantu Buragohain, R. Aditya Reddy, Yenduri Venkatesh, Gudmalwar Ashishkumar Prabhakar & Ch. V. Rama Rao

Authors
  1. Rantu Buragohain
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  2. R. Aditya Reddy
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  3. Yenduri Venkatesh
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  4. Gudmalwar Ashishkumar Prabhakar
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  5. Ch. V. Rama Rao
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Corresponding author

Correspondence to Ch. V. Rama Rao .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Central University of Karnataka, Gulbarga, India

    Ravindra Hegadi

  3. Indian Statistical Institute, Kolkata, India

    Umapada Pal

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Buragohain, R., Reddy, R.A., Venkatesh, Y., Prabhakar, G.A., Rao, C.V.R. (2022). Single Channel Speech Enhancement Using Masking Based on Sinusoidal Modeling. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-07005-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07004-4

  • Online ISBN: 978-3-031-07005-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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