D-Index & Metrics Best Publications

D-Index & Metrics

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 50 Citations 13,450 217 World Ranking 2894 National Ranking 56

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Source separation, Speech recognition, Blind signal separation, Algorithm and Non-negative matrix factorization are his primary areas of study. His study in Source separation is interdisciplinary in nature, drawing from both Estimation theory, Independent component analysis and SIGNAL. The study incorporates disciplines such as Audio signal processing, Reverberation, Microphone array, Microphone and Speech enhancement in addition to Speech recognition.

The various areas that Emmanuel Vincent examines in his Algorithm study include Audio signal and Signal processing. Combining a variety of fields, including Non-negative matrix factorization, Artificial intelligence and Fundamental frequency, are what the author presents in his essays. His work deals with themes such as Multiplicative function, Machine learning, Markov process and Spectral envelope, which intersect with Artificial intelligence.

His most cited work include:

  • Performance measurement in blind audio source separation (1896 citations)
  • The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines (429 citations)
  • Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR (363 citations)

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

Emmanuel Vincent mainly focuses on Speech recognition, Artificial intelligence, Source separation, Pattern recognition and Algorithm. His research integrates issues of Speech enhancement, Reverberation and Signal processing in his study of Speech recognition. Emmanuel Vincent studied Artificial intelligence and Music information retrieval that intersect with Segmentation.

His Source separation research is multidisciplinary, incorporating elements of Audio signal processing, Independent component analysis, Covariance function and Blind signal separation. His Pattern recognition study combines topics from a wide range of disciplines, such as Acoustic model, Covariance matrix, Noise and Expectation–maximization algorithm. His Algorithm research integrates issues from Artificial neural network, Time–frequency analysis and Spectrogram.

He most often published in these fields:

  • Speech recognition (45.80%)
  • Artificial intelligence (38.81%)
  • Source separation (33.22%)

What were the highlights of his more recent work (between 2019-2021)?

  • Speech recognition (45.80%)
  • Word error rate (8.74%)
  • Artificial intelligence (38.81%)

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

Speech recognition, Word error rate, Artificial intelligence, Generalization and Deep learning are his primary areas of study. In general Speech recognition study, his work on Speaker diarisation often relates to the realm of Set, thereby connecting several areas of interest. Within one scientific family, Emmanuel Vincent focuses on topics pertaining to Scheme under Word error rate, and may sometimes address concerns connected to Decoding methods, Selection, Space and Data mining.

Emmanuel Vincent has included themes like Machine learning, Open source, Cloning and Pattern recognition in his Artificial intelligence study. His research in Generalization intersects with topics in Ambient noise level, Joint, Voice activity detection and Test set. His biological study spans a wide range of topics, including Contrast, Identity, Utterance and Separation.

Between 2019 and 2021, his most popular works were:

  • CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings (49 citations)
  • LibriMix: An Open-Source Dataset for Generalizable Speech Separation (18 citations)
  • CHiME-6 Challenge: Tackling multispeaker speech recognition for unsegmented recordings (17 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

Emmanuel Vincent spends much of his time researching Speech recognition, Speech enhancement, Open source, Speaker diarisation and Natural. His Speech recognition research includes elements of Filter bank and Set. He combines subjects such as Software architecture, Source separation and Computer engineering with his study of Speech enhancement.

His Open source research is multidisciplinary, incorporating perspectives in Ambient noise level, Generalization, Separation and Extension. Emmanuel Vincent has researched Speaker diarisation in several fields, including Synchronization and Conversational speech.

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

Performance measurement in blind audio source separation

E. Vincent;R. Gribonval;C. Fevotte.
IEEE Transactions on Audio, Speech, and Language Processing (2006)

2627 Citations

The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

Jon Barker;Ricard Marxer;Emmanuel Vincent;Shinji Watanabe.
ieee automatic speech recognition and understanding workshop (2015)

518 Citations

Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model

Ngoc Q K Duong;Emmanuel Vincent;Rémi Gribonval.
IEEE Transactions on Audio, Speech, and Language Processing (2010)

414 Citations

DCASE 2017 Challenge setup: Tasks, datasets and baseline system

Annamaria Mesaros;Toni Heittola;Aleksandr Diment;Benjamin Elizalde.
DCASE 2017 - Workshop on Detection and Classification of Acoustic Scenes and Events (2017)

389 Citations

Subjective and Objective Quality Assessment of Audio Source Separation

V. Emiya;E. Vincent;N. Harlander;V. Hohmann.
IEEE Transactions on Audio, Speech, and Language Processing (2011)

349 Citations

Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR

Felix Weninger;Hakan Erdogan;Shinji Watanabe;Emmanuel Vincent.
international conference on latent variable analysis and signal separation (2015)

333 Citations

A General Flexible Framework for the Handling of Prior Information in Audio Source Separation

A. Ozerov;E. Vincent;F. Bimbot.
IEEE Transactions on Audio, Speech, and Language Processing (2012)

326 Citations

A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

Sharon Gannot;Emmanuel Vincent;Shmulik Markovich-Golan;Alexey Ozerov.
IEEE Transactions on Audio, Speech, and Language Processing (2017)

295 Citations

Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation

E. Vincent;N. Bertin;R. Badeau.
IEEE Transactions on Audio, Speech, and Language Processing (2010)

294 Citations

The second ‘chime’ speech separation and recognition challenge: Datasets, tasks and baselines

Emmanuel Vincent;Jon Barker;Shinji Watanabe;Jonathan Le Roux.
international conference on acoustics, speech, and signal processing (2013)

273 Citations

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