World's Best Scientists 2026 revealed!

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Computer Science

D-Index
53
Citations
12757
World Ranking
4773
National Ranking
2221

Research.com Recognitions

  • 2015 - IEEE Fellow For contributions to audio source separation and audio processing

Overview

Paris Smaragdis is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research primarily lies within the field of computer science, with a focus on signal processing and related subfields such as artificial intelligence and computer vision and pattern recognition.

Their main topics of research encompass speech and audio processing, speech recognition and synthesis, music and audio processing, advanced adaptive filtering techniques, blind source separation techniques, as well as music technology and sound studies. The breadth of their work addresses various aspects of audio and speech signal processing.

The scientist has contributed extensively to academic literature, publishing numerous papers in several venues. Notable publication venues include:

  • arXiv (Cornell University)
  • The Journal of the Acoustical Society of America
  • Interspeech 2022
  • IEEE Signal Processing Magazine
  • IEEE/ACM Transactions on Audio Speech and Language Processing

Among their recent papers are:

  • "RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing," 2022, arXiv (Cornell University)
  • "Audio Signal Processing in the 21st Century: The important outcomes of the past 25 years," 2023, IEEE Signal Processing Magazine
  • "Meta-AF: Meta-Learning for Adaptive Filters," 2022, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Self-supervised Learning for Speech Enhancement," 2020, arXiv (Cornell University)
  • "Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model," 2022, Interspeech 2022

They have collaborated frequently with several co-authors, including:

  • Efthymios Tzinis
  • Jean-Marc Valin
  • Jonah Casebeer
  • Zhepei Wang
  • Arvindh Krishnaswamy

Paris Smaragdis has been recognized as an IEEE Fellow since 2015 for contributions to audio source separation and audio processing.

Best Publications

  • Non-negative matrix factorization for polyphonic music transcription

    P. Smaragdis;J.C. Brown;J.C. Brown

  • Blind separation of convolved mixtures in the frequency domain

    Paris Smaragdis

  • Joint optimization of masks and deep recurrent neural networks for monaural source separation

    Po-Sen Huang;Minje Kim;Mark Hasegawa-Johnson;Paris Smaragdis

  • Deep learning for monaural speech separation

    Po Sen Huang;Minje Kim;Mark Hasegawa-Johnson;Paris Smaragdis

  • Convolutive Speech Bases and Their Application to Supervised Speech Separation

    P. Smaragdis

  • Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization

    Nasser Mohammadiha;Paris Smaragdis;Arne Leijon

  • Non-negative matrix factor deconvolution; Extraction of multiple sound sources from monophonic inputs

    Paris Smaragdis

  • Singing-voice separation from monaural recordings using robust principal component analysis

    Po-Sen Huang;Scott Deeann Chen;Paris Smaragdis;Mark Hasegawa-Johnson

  • Speech denoising using nonnegative matrix factorization with priors

    K.W. Wilson;B. Raj;P. Smaragdis;A. Divakaran

  • Supervised and semi-supervised separation of sounds from single-channel mixtures

    Paris Smaragdis;Bhiksha Raj;Madhusudana Shashanka

  • Audio analysis for surveillance applications

    R. Radhakrishnan;A. Divakaran;A. Smaragdis

  • Bitwise Neural Networks

    Minje Kim;Paris Smaragdis

  • Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view

    Paris Smaragdis;Cedric Fevotte;Gautham J. Mysore;Nasser Mohammadiha

  • Evaluation of blind signal separation methods

    Dwe Daniël Schobben;K Torkkola;P Smaragdis

  • A Probabilistic Latent Variable Model for Acoustic Modeling

    P. Smaragdis;B. Raj;M. Shashanka

  • Sudo RM -RF: Efficient Networks for Universal Audio Source Separation

    Efthymios Tzinis;Zhepei Wang;Paris Smaragdis

  • Probabilistic latent variable models as nonnegative factorizations.

    Madhusudana V. S. Shashanka;Bhiksha Raj;Paris Smaragdis

  • Non-negative hidden Markov modeling of audio with application to source separation

    Gautham J. Mysore;Paris Smaragdis;Bhiksha Raj

  • Singing-voice separation from monaural recordings using deep recurrent neural networks

    Po Sen Huang;Minje Kim;Mark Hasegawa-Johnson;Paris Smaragdis

  • Experiments on deep learning for speech denoising

    Ding Liu;Paris Smaragdis;Minje Kim

  • Combining Musical and Cultural Features for Intelligent Style Detection.

    Brian Whitman;Paris Smaragdis

Frequent Co-Authors

Bhiksha Raj
Bhiksha Raj Carnegie Mellon University
Petros T. Boufounos
Petros T. Boufounos Mitsubishi Electric (United States)
Mark Hasegawa-Johnson
Mark Hasegawa-Johnson University of Illinois at Urbana-Champaign
Malcolm Slaney
Malcolm Slaney Stanford University
Cédric Févotte
Cédric Févotte Toulouse Institute of Computer Science Research
Simon Doclo
Simon Doclo Carl von Ossietzky University of Oldenburg
Arthur F. Kramer
Arthur F. Kramer Northeastern University
Rob A. Rutenbar
Rob A. Rutenbar University of Pittsburgh
Roy H. Campbell
Roy H. Campbell University of Illinois at Urbana-Champaign
Peter Vary
Peter Vary RWTH Aachen University

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