What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Signal
His scientific interests lie mostly in Blind signal separation, Speech recognition, Independent component analysis, Source separation and Artificial intelligence.
His study in Blind signal separation is interdisciplinary in nature, drawing from both Underdetermined system, Algorithm, Frequency domain and Reverberation.
His Speech recognition study combines topics in areas such as Smoothing, Transient response and Interference.
His Independent component analysis study integrates concerns from other disciplines, such as Maximum a posteriori estimation and Signal processing.
His Source separation research is multidisciplinary, incorporating perspectives in Deconvolution and Time–frequency analysis.
The various areas that Shoji Makino examines in his Artificial intelligence study include Estimation theory and Pattern recognition.
His most cited work include:
- A robust and precise method for solving the permutation problem of frequency-domain blind source separation (539 citations)
- The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech (304 citations)
- Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment (282 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Speech recognition, Blind signal separation, Artificial intelligence, Acoustics and Independent component analysis.
His Speech recognition research incorporates elements of Speech enhancement, Noise, Brain–computer interface and Reverberation.
His research in Blind signal separation intersects with topics in Underdetermined system, Source separation, Algorithm, Frequency domain and Signal processing.
Shoji Makino works mostly in the field of Artificial intelligence, limiting it down to topics relating to Pattern recognition and, in certain cases, Cluster analysis and Direction of arrival, as a part of the same area of interest.
His studies deal with areas such as Microphone array, Signal, Echo, Microphone and Impulse response as well as Acoustics.
Shoji Makino has included themes like Estimation theory and Permutation in his Independent component analysis study.
He most often published in these fields:
- Speech recognition (40.05%)
- Blind signal separation (29.13%)
- Artificial intelligence (23.79%)
What were the highlights of his more recent work (between 2012-2021)?
- Speech recognition (40.05%)
- Brain–computer interface (14.32%)
- Artificial intelligence (23.79%)
In recent papers he was focusing on the following fields of study:
Shoji Makino mainly investigates Speech recognition, Brain–computer interface, Artificial intelligence, Electroencephalography and Pattern recognition.
His research integrates issues of Speech enhancement, Microphone array, Noise reduction and Non-negative matrix factorization in his study of Speech recognition.
His work carried out in the field of Microphone array brings together such families of science as Amplitude, Algorithm, Fourier transform and Blind signal separation.
His Artificial intelligence research is multidisciplinary, incorporating elements of Photosensitive epilepsy and Computer vision.
Shoji Makino regularly ties together related areas like Source separation in his Pattern recognition studies.
The concepts of his Source separation study are interwoven with issues in Independent component analysis, Autoencoder and Spectrogram.
Between 2012 and 2021, his most popular works were:
- Multichannel Signal Enhancement Algorithms for Assisted Listening Devices: Exploiting spatial diversity using multiple microphones (87 citations)
- Comparison of P300 Responses in Auditory, Visual and Audiovisual Spatial Speller BCI Paradigms (38 citations)
- Blind compensation of interchannel sampling frequency mismatch for ad hoc microphone array based on maximum likelihood estimation (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Signal
His main research concerns Speech recognition, Brain–computer interface, Electroencephalography, Artificial intelligence and Pattern recognition.
Shoji Makino works in the field of Speech recognition, focusing on Speech processing in particular.
His Artificial intelligence study frequently links to adjacent areas such as Polynomial.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Artificial neural network, Autoencoder and Source separation.
His Microphone array study combines topics in areas such as Acoustics, Speech enhancement, Algorithm and Synchronization.
Shoji Makino merges Blind signal separation with Cloud storage in his research.
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