What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Algorithm
His primary areas of study are Algorithm, Non-negative matrix factorization, Speech recognition, Source separation and Matrix decomposition.
The concepts of his Algorithm study are interwoven with issues in Subspace topology, Unsupervised learning, Probability measure and Signal processing.
Roland Badeau focuses mostly in the field of Non-negative matrix factorization, narrowing it down to matters related to Multiplicative function and, in some cases, Mathematical optimization, Optimization problem and Exponential stability.
His Speech recognition research integrates issues from Feature extraction and Sound recording and reproduction.
His Source separation research is multidisciplinary, incorporating perspectives in Mean squared error, Probabilistic logic, Applied mathematics and Spectrogram.
Roland Badeau has included themes like Audio signal processing, Musical instrument, Artificial intelligence and Pattern recognition in his Matrix decomposition study.
His most cited work include:
- Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle (242 citations)
- Adaptive Harmonic Spectral Decomposition for Multiple Pitch Estimation (211 citations)
- Enforcing Harmonicity and Smoothness in Bayesian Non-Negative Matrix Factorization Applied to Polyphonic Music Transcription (173 citations)
What are the main themes of his work throughout his whole career to date?
Roland Badeau mostly deals with Algorithm, Speech recognition, Non-negative matrix factorization, Source separation and Artificial intelligence.
His Algorithm research is multidisciplinary, incorporating elements of Subspace topology, Spectrogram, Mathematical optimization, Signal processing and Audio signal.
The Spectral envelope research he does as part of his general Speech recognition study is frequently linked to other disciplines of science, such as Transcription, therefore creating a link between diverse domains of science.
His Non-negative matrix factorization research is included under the broader classification of Matrix decomposition.
His studies deal with areas such as Wiener filter, Estimator, Fourier transform, Mixture model and Mixing as well as Source separation.
Expectation–maximization algorithm and Harmonic is closely connected to Pattern recognition in his research, which is encompassed under the umbrella topic of Artificial intelligence.
He most often published in these fields:
- Algorithm (45.25%)
- Speech recognition (29.05%)
- Non-negative matrix factorization (27.37%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (45.25%)
- Source separation (27.37%)
- Reverberation (9.50%)
In recent papers he was focusing on the following fields of study:
Roland Badeau spends much of his time researching Algorithm, Source separation, Reverberation, Non-negative matrix factorization and Acoustics.
His Algorithm study combines topics from a wide range of disciplines, such as Probabilistic logic, Fourier transform, Statistical model, Speech enhancement and Monte Carlo method.
His Fourier transform study typically links adjacent topics like Speech recognition.
His Speech recognition research incorporates themes from Acoustic source localization and Joint.
His Source separation study is focused on Artificial intelligence in general.
He is exploring Non-negative matrix factorization as part of his Matrix decomposition and Algebra and Non-negative matrix factorization studies.
Between 2016 and 2021, his most popular works were:
- Generalized Sliced Wasserstein Distances (51 citations)
- Model-Based STFT Phase Recovery for Audio Source Separation (25 citations)
- Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance (19 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Artificial intelligence
The scientist’s investigation covers issues in Algorithm, Source separation, Fourier transform, Wiener filter and Probability measure.
His Algorithm study incorporates themes from Data modeling, Spurious relationship and Mathematical optimization.
He has included themes like Non-negative matrix factorization, Random variable and Pattern recognition in his Source separation study.
The Fourier transform study combines topics in areas such as Time–frequency analysis, Speech recognition, Statistical model, Monte Carlo method and Audio signal.
His Time–frequency analysis research focuses on Time domain and how it relates to Reverberation, Blind signal separation, Modified discrete cosine transform and Matrix decomposition.
His Speech recognition research includes themes of Weighting, Convolution, Mixing and Spectral density.
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