What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Artificial intelligence, Pattern recognition, Source separation, Speech recognition and Audio signal processing are his primary areas of study.
When carried out as part of a general Artificial intelligence research project, his work on Speaker diarisation and Artificial neural network is frequently linked to work in Structure, therefore connecting diverse disciplines of study.
His Speaker diarisation research incorporates themes from Segmentation, Normalization and Natural language processing.
Frédéric Bimbot interconnects Estimation theory, Statistical model and Iterative method in the investigation of issues within Pattern recognition.
His Source separation study combines topics from a wide range of disciplines, such as Hidden Markov model, Non-negative matrix factorization and Blind signal separation.
His work on Speech processing, Speaker recognition and Phonetic transcription as part of general Speech recognition research is frequently linked to Sequence matching, bridging the gap between disciplines.
His most cited work include:
- A tutorial on text-independent speaker verification (698 citations)
- The BANCA database and evaluation protocol (391 citations)
- A General Flexible Framework for the Handling of Prior Information in Audio Source Separation (244 citations)
What are the main themes of his work throughout his whole career to date?
Frédéric Bimbot mostly deals with Speech recognition, Artificial intelligence, Pattern recognition, Speaker recognition and Natural language processing.
His research on Speech recognition frequently connects to adjacent areas such as Time delay neural network.
His Artificial intelligence research is multidisciplinary, incorporating elements of Context and Machine learning.
His work carried out in the field of Pattern recognition brings together such families of science as Acoustic model, Covariance matrix and Cluster analysis.
His Speaker recognition research integrates issues from NIST and Field.
Frédéric Bimbot focuses mostly in the field of Natural language processing, narrowing it down to matters related to Music information retrieval and, in some cases, Chord.
He most often published in these fields:
- Speech recognition (56.25%)
- Artificial intelligence (51.39%)
- Pattern recognition (27.78%)
What were the highlights of his more recent work (between 2011-2019)?
- Speech recognition (56.25%)
- Artificial intelligence (51.39%)
- Source separation (14.58%)
In recent papers he was focusing on the following fields of study:
Frédéric Bimbot focuses on Speech recognition, Artificial intelligence, Source separation, Music information retrieval and Natural language processing.
Many of his research projects under Speech recognition are closely connected to Latency with Latency, tying the diverse disciplines of science together.
His Pattern recognition research extends to the thematically linked field of Artificial intelligence.
His work deals with themes such as Database construction and Facial expression, which intersect with Pattern recognition.
His Source separation study incorporates themes from Computer engineering, Blind signal separation, Toolbox, Audio signal flow and Matrix decomposition.
His Music information retrieval study deals with Theoretical computer science intersecting with Graph, Chord, Communication and Matrix.
Between 2011 and 2019, his most popular works were:
- A General Flexible Framework for the Handling of Prior Information in Audio Source Separation (244 citations)
- From Blind to Guided Audio Source Separation: How models and side information can improve the separation of sound (114 citations)
- The Flexible Audio Source Separation Toolbox Version 2.0 (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Frédéric Bimbot spends much of his time researching Speech recognition, Source separation, Speech processing, Artificial intelligence and Data mining.
His Speech recognition research is multidisciplinary, incorporating perspectives in Reverberation and Pattern matching.
Frédéric Bimbot has researched Source separation in several fields, including Toolbox, Non-negative matrix factorization, Audio signal processing and Computer engineering.
His Computer engineering research is multidisciplinary, relying on both Machine learning and Hidden Markov model.
The various areas that Frédéric Bimbot examines in his Speech processing study include Cluster analysis, Mel-frequency cepstrum, Unsupervised learning, Pattern recognition and Automatic summarization.
Frédéric Bimbot has included themes like Semiotics and Notation in his Artificial intelligence study.
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