What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Speech recognition
His scientific interests lie mostly in Speech recognition, Fundamental frequency, Artificial intelligence, Noise reduction and Electroencephalography.
Alain de Cheveigné has included themes like Stimulus and Auditory system in his Speech recognition study.
His Fundamental frequency research incorporates themes from Time domain, Algorithm, Instantaneous phase and Harmonic.
His Artificial intelligence study incorporates themes from Distortion and Computer vision.
His work deals with themes such as Covariance, Decorrelation and Pattern recognition, which intersect with Electroencephalography.
His research integrates issues of Signal, Estimator, Pitch Discrimination and Autocorrelation in his study of Speech processing.
His most cited work include:
- Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: possible role of a repetitive structure in sounds (1627 citations)
- YIN, a fundamental frequency estimator for speech and music (1465 citations)
- Denoising based on time-shift PCA (167 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Speech recognition, Artificial intelligence, Electroencephalography, Pattern recognition and Acoustics.
His studies deal with areas such as Stimulus, Decoding methods and Perception as well as Speech recognition.
His Artificial intelligence research integrates issues from Computer vision and Magnetoencephalography.
His research investigates the link between Electroencephalography and topics such as Covariance that cross with problems in Decorrelation and Chart.
His study in Acoustics focuses on Fundamental frequency in particular.
His Fundamental frequency research incorporates elements of Algorithm, Instantaneous phase, Musical acoustics and Harmonic.
He most often published in these fields:
- Speech recognition (59.42%)
- Artificial intelligence (52.17%)
- Electroencephalography (40.58%)
What were the highlights of his more recent work (between 2017-2021)?
- Electroencephalography (40.58%)
- Speech recognition (59.42%)
- Artificial intelligence (52.17%)
In recent papers he was focusing on the following fields of study:
Alain de Cheveigné mainly investigates Electroencephalography, Speech recognition, Artificial intelligence, Pattern recognition and Magnetoencephalography.
His study focuses on the intersection of Electroencephalography and fields such as Stimulus with connections in the field of Adaptive beamformer.
With his scientific publications, his incorporates both Speech recognition and Phonotactics.
When carried out as part of a general Artificial intelligence research project, his work on Artifact, Voxel, Principal component analysis and Synthetic data is frequently linked to work in Component, therefore connecting diverse disciplines of study.
In the subject of general Pattern recognition, his work in Dimensionality reduction is often linked to Overfitting and Variance, thereby combining diverse domains of study.
His Magnetoencephalography research includes themes of Effective method, Spatial filter and Computer vision.
Between 2017 and 2021, his most popular works were:
- Decoding the auditory brain with canonical component analysis (77 citations)
- A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding. (47 citations)
- Filters: When, Why, and How (Not) to Use Them. (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Alain de Cheveigné mostly deals with Electroencephalography, Artificial intelligence, Pattern recognition, Filter and Ringing.
The concepts of his Electroencephalography study are interwoven with issues in Speech recognition and Canonical correlation.
Alain de Cheveigné combines subjects such as Stimulus, Decoding methods and Auditory perception with his study of Speech recognition.
His work in the fields of Artificial intelligence, such as Anomaly detection and Artifact, intersects with other areas such as Ringing artifacts and Inpainting.
His Pattern recognition research is multidisciplinary, incorporating elements of Synthetic data and Interpolation.
His study in Filter is interdisciplinary in nature, drawing from both Step response and Time–frequency representation.
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.
