D-Index & Metrics Best Publications
Computer Science
France
2023

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 51 Citations 7,959 298 World Ranking 3576 National Ranking 74

Research.com Recognitions

Awards & Achievements

2023 - Research.com Computer Science in France Leader Award

2017 - IEEE Fellow For contributions to analysis, indexing and decomposition of audio and music signals

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Gael Richard focuses on Speech recognition, Audio signal processing, Artificial intelligence, Source separation and Algorithm. Gael Richard mostly deals with Speech processing in his studies of Speech recognition. His Audio signal processing study combines topics from a wide range of disciplines, such as Transcription, Sound recording and reproduction and Music information retrieval.

Gael Richard combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence. His Source separation research is multidisciplinary, incorporating perspectives in Wiener filter, Blind signal separation and Spectrogram. The concepts of his Algorithm study are interwoven with issues in Calculus and Signal processing.

His most cited work include:

  • Events Detection for an Audio-Based Surveillance System (260 citations)
  • Signal Processing for Music Analysis (179 citations)
  • Fast approximated power iteration subspace tracking (150 citations)

What are the main themes of his work throughout his whole career to date?

The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Pattern recognition, Algorithm and Source separation. His biological study deals with issues like Audio signal processing, which deal with fields such as Sound recording and reproduction. In Artificial intelligence, he works on issues like Non-negative matrix factorization, which are connected to Feature learning.

Gael Richard works in the field of Pattern recognition, namely Mixture model. His Algorithm research incorporates elements of Subspace topology and Signal processing. His Source separation research is multidisciplinary, incorporating elements of Reverberation and Blind signal separation.

He most often published in these fields:

  • Speech recognition (45.60%)
  • Artificial intelligence (32.57%)
  • Pattern recognition (20.52%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (32.57%)
  • Speech recognition (45.60%)
  • Non-negative matrix factorization (11.07%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Speech recognition, Non-negative matrix factorization, Source separation and Matrix decomposition are his primary areas of study. His studies in Artificial intelligence integrate themes in fields like Audio signal, Computer vision and Pattern recognition. He has included themes like Artificial neural network and Time–frequency analysis in his Pattern recognition study.

His study on Intelligibility is often connected to Process as part of broader study in Speech recognition. Gael Richard combines subjects such as Channel and Spectrogram with his study of Non-negative matrix factorization. His Source separation research is multidisciplinary, incorporating elements of Mixing, Reverberation and Blind signal separation.

Between 2015 and 2021, his most popular works were:

  • Acoustic scene classification with matrix factorization for unsupervised feature learning (52 citations)
  • Feature Learning With Matrix Factorization Applied to Acoustic Scene Classification (47 citations)
  • Motion informed audio source separation (27 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Gael Richard mostly deals with Artificial intelligence, Non-negative matrix factorization, Pattern recognition, Speech recognition and Matrix decomposition. His work on Feature learning, Embodied cognition and Association rule learning is typically connected to Dialog system and Head movements as part of general Artificial intelligence study, connecting several disciplines of science. The concepts of his Non-negative matrix factorization study are interwoven with issues in Euclidean distance, Divergence, Source separation and Spectrogram.

His Pattern recognition research is multidisciplinary, relying on both Artificial neural network and Speech enhancement. Hidden Markov model is the focus of his Speech recognition research. His biological study focuses on Audio signal processing.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Events Detection for an Audio-Based Surveillance System

C. Clavel;T. Ehrette;G. Richard.
international conference on multimedia and expo (2005)

433 Citations

Events Detection for an Audio-Based Surveillance System

C. Clavel;T. Ehrette;G. Richard.
international conference on multimedia and expo (2005)

433 Citations

Signal Processing for Music Analysis

M. Muller;D. P. W. Ellis;A. Klapuri;G. Richard.
IEEE Journal of Selected Topics in Signal Processing (2011)

330 Citations

Signal Processing for Music Analysis

M. Muller;D. P. W. Ellis;A. Klapuri;G. Richard.
IEEE Journal of Selected Topics in Signal Processing (2011)

330 Citations

Melody Extraction from Polyphonic Music Signals: Approaches, applications, and challenges

Justin Salamon;Emilia Gomez;Daniel P. W. Ellis;Gael Richard.
IEEE Signal Processing Magazine (2014)

249 Citations

Melody Extraction from Polyphonic Music Signals: Approaches, applications, and challenges

Justin Salamon;Emilia Gomez;Daniel P. W. Ellis;Gael Richard.
IEEE Signal Processing Magazine (2014)

249 Citations

Fast approximated power iteration subspace tracking

R. Badeau;B. David;G. Richard.
IEEE Transactions on Signal Processing (2005)

241 Citations

Fast approximated power iteration subspace tracking

R. Badeau;B. David;G. Richard.
IEEE Transactions on Signal Processing (2005)

241 Citations

Source/Filter Model for Unsupervised Main Melody Extraction From Polyphonic Audio Signals

J.-L. Durrieu;G. Richard;B. David;C. Fevotte.
IEEE Transactions on Audio, Speech, and Language Processing (2010)

234 Citations

Source/Filter Model for Unsupervised Main Melody Extraction From Polyphonic Audio Signals

J.-L. Durrieu;G. Richard;B. David;C. Fevotte.
IEEE Transactions on Audio, Speech, and Language Processing (2010)

234 Citations

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