Jishnu

Jishnu Sadasivan

Biography

Jishnu Sadasivan received the B.Tech. degree in electronics and communications engineering from Cochin University of Science and Technology, Kerala, India, in 2007, the M.Tech. degree in signal processing from the College of Engineering Trivandrum, Thiruvananthapuram, India, in 2009, and the Ph.D. degree from Indian Institute of Science, Bengaluru, India, in 2018.

He is currently a Senior Researcher with Ericsson Research, Bengaluru. His research interests include signal processing, speech processing, and artificial intelligence.

Publications

2021

  1. IEEE TSP 21
    Adaptive Savitzky-Golay filtering in non-Gaussian noise
    A. John , , and 
    IEEE Transactions on Signal Processing, Apr 2021
    Cite Download .bib
    @article{john2021adaptive, author = {John, A. and Sadasivan, J. and Seelamantula, C. S.}, journal = {IEEE Transactions on Signal Processing}, pages = {5021--5036}, title = {Adaptive Savitzky-Golay filtering in non-Gaussian noise}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:_5tno0g5mFcC}, volume = {69}, year = {2021} }

2020

  1. Speech Commun. 20
    Speech enhancement using a risk estimation approach
    , , and N. R. Muraka
    Speech Communication, Apr 2020
    Cite Download .bib
    @article{sadasivan2020speech, author = {Sadasivan, J. and Seelamantula, C. S. and Muraka, N. R.}, journal = {Speech Communication}, pages = {12--29}, title = {Speech enhancement using a risk estimation approach}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ruyezt5ZtCIC}, volume = {116}, year = {2020} }
  2. Speech Commun. 20
    Musical noise suppression using a low-rank and sparse matrix decomposition approach
    , J. K. Dhiman , and 
    Speech Communication, Apr 2020
    Cite Download .bib
    @article{sadasivan2020musical, author = {Sadasivan, J. and Dhiman, J. K. and Seelamantula, C. S.}, journal = {Speech Communication}, pages = {41--52}, title = {Musical noise suppression using a low-rank and sparse matrix decomposition approach}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:4fGpz3EwCPoC}, volume = {125}, year = {2020} }
  3. APSIPA Trans. Signal Inf. Process. 20
    Signal denoising using the minimum-probability-of-error criterion
    , , and 
    APSIPA Transactions on Signal and Information Processing, Apr 2020
    Cite Download .bib
    @article{sadasivan2020signal, author = {Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.}, journal = {APSIPA Transactions on Signal and Information Processing}, pages = {e3}, title = {Signal denoising using the minimum-probability-of-error criterion}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:Fu2w8maKXqMC}, volume = {9}, year = {2020} }
  4. ICASSP 20
    Epoch Estimation from a Speech Signal Using Gammatone Wavelets in a Scattering Network
    P. Kulkarni , , , and 
    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2020
    Cite Download .bib
    @inproceedings{kulkarni2020epoch, author = {Kulkarni, P. and Sadasivan, J. and Adiga, A. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {Epoch Estimation from a Speech Signal Using Gammatone Wavelets in a Scattering Network}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:FPJr55Dyh1AC}, year = {2020} }

2018

  1. INTERSPEECH 18
    Speech Enhancement Using the Minimum-probability-of-error Criterion
    , , and 
    In INTERSPEECH, Apr 2018
    Cite Download .bib
    @inproceedings{sadasivan2018speech, author = {Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.}, booktitle = {INTERSPEECH}, pages = {1141--1145}, title = {Speech Enhancement Using the Minimum-probability-of-error Criterion}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:EYYDruWGBe4C}, year = {2018} }

2017

  1. arXiv 17
    PROSE: Perceptual Risk Optimization for Speech Enhancement
    , , and N. R. Muraka
    arXiv preprint arXiv:1710.03975, Apr 2017
    Cite Download .bib
    @article{sadasivan2017prose, author = {Sadasivan, J. and Seelamantula, C. S. and Muraka, N. R.}, journal = {arXiv preprint arXiv:1710.03975}, title = {PROSE: Perceptual Risk Optimization for Speech Enhancement}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:tKAzc9rXhukC}, year = {2017} }

2016

  1. ICASSP 16
    Joint dictionary training for bandwidth extension of speech signals
    , , and 
    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2016
    Cite Download .bib
    @inproceedings{sadasivan2016joint, author = {Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {Joint dictionary training for bandwidth extension of speech signals}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:bnK-pcrLprsC}, year = {2016} }
  2. ICASSP 16
    An unbiased risk estimator for Gaussian mixture noise distributions—Application to speech denoising
    , and 
    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2016
    Cite Download .bib
    @inproceedings{sadasivan2016unbiased, author = {Sadasivan, J. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {An unbiased risk estimator for Gaussian mixture noise distributions—Application to speech denoising}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:olpn-zPbct0C}, year = {2016} }
  3. INTERSPEECH 16
    A Novel Risk-Estimation-Theoretic Framework for Speech Enhancement in Nonstationary and Non-Gaussian Noise Conditions
    , and 
    In INTERSPEECH, Apr 2016
    Cite Download .bib
    @inproceedings{sadasivan2016novel, author = {Sadasivan, J. and Seelamantula, C. S.}, booktitle = {INTERSPEECH}, pages = {3718--3722}, title = {A Novel Risk-Estimation-Theoretic Framework for Speech Enhancement in Nonstationary and Non-Gaussian Noise Conditions}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ZuybSZzF8UAC}, year = {2016} }

2014

  1. ICASSP 14
    An optimum shrinkage estimator based on minimum-probability-of-error criterion and application to signal denoising
    , , and 
    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2014
    Cite Download .bib
    @inproceedings{sadasivan2014optimum, author = {Sadasivan, J. and Mukherjee, S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {An optimum shrinkage estimator based on minimum-probability-of-error criterion and application to signal denoising}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:pqnbT2bcN3wC}, year = {2014} }