What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
His main research concerns Speech recognition, Algorithm, Non-negative matrix factorization, Spectrogram and Source separation.
His study in the field of Hidden Markov model is also linked to topics like Musical acoustics.
His Algorithm research includes elements of Amplitude, Independent component analysis, Nonnegative matrix and Blind signal separation.
He interconnects Artificial intelligence and Pattern recognition in the investigation of issues within Non-negative matrix factorization.
Nobutaka Ono combines subjects such as Speech enhancement, Mixing, Harmonic and Audio signal with his study of Spectrogram.
His Source separation study combines topics in areas such as Audio signal processing and Time–frequency analysis.
His most cited work include:
- Stable and fast update rules for independent vector analysis based on auxiliary function technique (210 citations)
- Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization (170 citations)
- Complex NMF: A new sparse representation for acoustic signals (127 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, Algorithm, Acoustics and Pattern recognition.
His study in Speech recognition is interdisciplinary in nature, drawing from both Noise reduction, Noise and Harmonic.
His work in Artificial intelligence addresses subjects such as Non-negative matrix factorization, which are connected to disciplines such as Sparse matrix.
His Algorithm research is multidisciplinary, incorporating perspectives in Low-rank approximation, Independent component analysis, Matrix analysis and Blind signal separation.
Nobutaka Ono has included themes like Microphone array, Noise-canceling microphone, Microphone and Signal processing in his Acoustics study.
His study focuses on the intersection of Spectrogram and fields such as Source separation with connections in the field of Audio signal processing and Time–frequency analysis.
He most often published in these fields:
- Speech recognition (36.14%)
- Artificial intelligence (28.42%)
- Algorithm (27.37%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (27.37%)
- Artificial intelligence (28.42%)
- Blind signal separation (14.39%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Algorithm, Artificial intelligence, Blind signal separation, Speech recognition and Speech enhancement.
His work on Covariance matrix is typically connected to Convergence as part of general Algorithm study, connecting several disciplines of science.
His studies deal with areas such as Computer vision and Pattern recognition as well as Artificial intelligence.
Noise measurement is closely connected to Non-negative matrix factorization in his research, which is encompassed under the umbrella topic of Blind signal separation.
His Speech recognition study frequently draws connections between related disciplines such as Contrast.
His Speech enhancement research incorporates themes from Microphone array, Microphone, Time–frequency analysis, Beamforming and Synchronization.
Between 2016 and 2021, his most popular works were:
- The 2016 Signal Separation Evaluation Campaign (100 citations)
- Determined Blind Source Separation with Independent Low-Rank Matrix Analysis (34 citations)
- Generalized independent low-rank matrix analysis using heavy-tailed distributions for blind source separation (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
His primary areas of study are Algorithm, Blind signal separation, Speech recognition, Artificial intelligence and Non-negative matrix factorization.
He is interested in Covariance matrix, which is a branch of Algorithm.
Within one scientific family, Nobutaka Ono focuses on topics pertaining to Low-rank approximation under Blind signal separation, and may sometimes address concerns connected to Multivariate normal distribution.
The various areas that Nobutaka Ono examines in his Speech recognition study include Speech enhancement and Synchronization.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Computer vision and Pattern recognition.
In his research on the topic of Pattern recognition, Source separation, Generative model, Sparse matrix, Matrix decomposition and Regularization is strongly related with Spectrogram.
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