What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Blind signal separation, Independent component analysis, Source separation, Speech recognition and Artificial intelligence are his primary areas of study.
His Blind signal separation study integrates concerns from other disciplines, such as Underdetermined system, Algorithm, Reverberation, Cluster analysis and Frequency domain.
His Independent component analysis research is multidisciplinary, incorporating perspectives in Curvilinear coordinates and Elliptic coordinate system.
He studied Source separation and Time–frequency analysis that intersect with Multivariate normal distribution, Sparse matrix, Matrix decomposition and Non-negative matrix factorization.
His Speech recognition research also works with subjects such as
- Noise and related Background noise,
- Smoothing which is related to area like Microphone, Monaural and Support vector machine.
His Artificial intelligence research integrates issues from Expectation–maximization algorithm, Direction of arrival, Sensor array 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)
- Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment (282 citations)
- Blind speech separation (252 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Blind signal separation, Artificial intelligence, Algorithm, Independent component analysis and Pattern recognition.
Hiroshi Sawada has researched Blind signal separation in several fields, including Underdetermined system, Speech recognition, Source separation, Frequency domain and Signal processing.
His studies link Machine learning with Artificial intelligence.
The concepts of his Algorithm study are interwoven with issues in Matrix decomposition, Matrix, Non-negative matrix factorization and Function.
His research integrates issues of Estimation theory, Bin, Convolution and Signal, Audio signal in his study of Independent component analysis.
His research in Pattern recognition intersects with topics in Normalization, Sensor array, Direction of arrival and Cluster analysis.
He most often published in these fields:
- Blind signal separation (42.17%)
- Artificial intelligence (32.93%)
- Algorithm (29.72%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (29.72%)
- Blind signal separation (42.17%)
- Non-negative matrix factorization (8.03%)
In recent papers he was focusing on the following fields of study:
Hiroshi Sawada spends much of his time researching Algorithm, Blind signal separation, Non-negative matrix factorization, Ferromagnetism and Matrix.
Hiroshi Sawada interconnects Frequency domain, Adaptive sampling and Kriging in the investigation of issues within Algorithm.
His study in Frequency domain is interdisciplinary in nature, drawing from both Direction of arrival, Microphone array, Norm and FastICA.
His studies in Blind signal separation integrate themes in fields like Independent component analysis, Matrix decomposition, Optimization problem, Matrix analysis and Diagonal matrix.
His Non-negative matrix factorization research includes themes of Missing data and Interpolation.
His Linear programming research focuses on Pattern recognition and how it relates to Source separation.
Between 2017 and 2021, his most popular works were:
- Determined Blind Source Separation with Independent Low-Rank Matrix Analysis (34 citations)
- A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF (26 citations)
- Independent Low-Rank Matrix Analysis with Decorrelation Learning (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Algorithm, Blind signal separation, Matrix analysis, Non-negative matrix factorization and Independent component analysis.
His Algorithm study incorporates themes from Function, Signal, Adaptive sampling and Kriging.
His Blind signal separation research incorporates elements of Permutation, Minification, Synchronization, Covariance matrix and Diagonal matrix.
His study focuses on the intersection of Matrix analysis and fields such as Low-rank approximation with connections in the field of Multivariate normal distribution, Extension, Decorrelation and Coordinate descent.
His research investigates the connection between Non-negative matrix factorization and topics such as Optimization problem that intersect with issues in Audio signal processing, Fourier transform, Matrix decomposition and Spectrogram.
The various areas that Hiroshi Sawada examines in his Independent component analysis study include Auxiliary function, Independent vector analysis, Scalar and Audio signal.
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