D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 6,330 374 World Ranking 6383 National Ranking 101

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Speech recognition

Hiroshi Saruwatari mostly deals with Speech recognition, Blind signal separation, Algorithm, Artificial intelligence and Speech processing. His Speech recognition research incorporates elements of Speech enhancement, Microphone array and Noise reduction. His research integrates issues of Independent component analysis, Frequency domain and Reverberation in his study of Blind signal separation.

His Algorithm research is multidisciplinary, incorporating elements of Low frequency and Beamforming. His Artificial intelligence study combines topics in areas such as Natural language processing, Loudspeaker and Pattern recognition. His work focuses on many connections between Speech processing and other disciplines, such as Finite impulse response, that overlap with his field of interest in Learning rule, Adaptive filter and Time domain.

His most cited work include:

  • The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech (304 citations)
  • Evaluation of blind signal separation method using directivity pattern under reverberant conditions (182 citations)
  • Blind source separation combining independent component analysis and beamforming (179 citations)

What are the main themes of his work throughout his whole career to date?

Hiroshi Saruwatari mainly investigates Speech recognition, Blind signal separation, Artificial intelligence, Algorithm and Independent component analysis. He specializes in Speech recognition, namely Speech processing. His Blind signal separation research focuses on subjects like Frequency domain, which are linked to Reverberation.

His work on Speech synthesis as part of general Artificial intelligence study is frequently linked to Non-negative matrix factorization, bridging the gap between disciplines. His study in the field of Covariance function is also linked to topics like Matrix analysis, Convergence and Low-rank approximation. His study in Pattern recognition is interdisciplinary in nature, drawing from both Separation and Spectrogram.

He most often published in these fields:

  • Speech recognition (57.28%)
  • Blind signal separation (27.32%)
  • Artificial intelligence (25.99%)

What were the highlights of his more recent work (between 2019-2021)?

  • Algorithm (19.21%)
  • Speech recognition (57.28%)
  • Artificial neural network (4.14%)

In recent papers he was focusing on the following fields of study:

His main research concerns Algorithm, Speech recognition, Artificial neural network, Blind signal separation and Speech synthesis. His study on Source separation is often connected to Non-negative matrix factorization, Matrix analysis and Basis as part of broader study in Algorithm. His Language model study in the realm of Speech recognition connects with subjects such as Domain adaptation.

In his research, Prosody is intimately related to Context, which falls under the overarching field of Artificial neural network. His Blind signal separation study combines topics from a wide range of disciplines, such as Multivariate normal distribution, Multivariate statistics and Generative model. He interconnects Mixture model, Spectral envelope and Automatic summarization in the investigation of issues within Speech synthesis.

Between 2019 and 2021, his most popular works were:

  • Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model for Determined Blind Source Separation (11 citations)
  • Multichannel Non-Negative Matrix Factorization Using Banded Spatial Covariance Matrices in Wavenumber Domain (5 citations)
  • Acoustic model-based subword tokenization and prosodic-context extraction without language knowledge for text-to-speech synthesis (2 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Speech recognition, Algorithm, Blind signal separation, Speech synthesis and Artificial neural network are his primary areas of study. His work carried out in the field of Speech recognition brings together such families of science as Backpropagation, Singing, Discriminator, Variety and Similarity. His Algorithm study incorporates themes from Weighting, Active noise control, Loudspeaker, Noise reduction and Kernel.

Blind signal separation and Covariance function are frequently intertwined in his study. His research investigates the connection with Speech synthesis and areas like Rule-based machine translation which intersect with concerns in Prosody. The concepts of his Artificial intelligence study are interwoven with issues in Multivariate statistics and Pattern recognition.

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.

Best Publications

The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech

S. Araki;R. Mukai;S. Makino;T. Nishikawa.
IEEE Transactions on Speech and Audio Processing (2003)

484 Citations

Evaluation of blind signal separation method using directivity pattern under reverberant conditions

S. Kurita;H. Saruwatari;S. Kajita;K. Takeda.
international conference on acoustics, speech, and signal processing (2000)

279 Citations

Blind source separation combining independent component analysis and beamforming

Hiroshi Saruwatari;Satoshi Kurita;Kazuya Takeda;Fumitada Itakura.
EURASIP Journal on Advances in Signal Processing (2003)

246 Citations

Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization

Daichi Kitamura;Nobutaka Ono;Hiroshi Sawada;Hirokazu Kameoka.
IEEE Transactions on Audio, Speech, and Language Processing (2016)

223 Citations

Blind source separation based on a fast-convergence algorithm combining ICA and beamforming

H. Saruwatari;T. Kawamura;T. Nishikawa;A. Lee.
IEEE Transactions on Audio, Speech, and Language Processing (2006)

220 Citations

Voice conversion algorithm based on Gaussian mixture model with dynamic frequency warping of STRAIGHT spectrum

T. Toda;H. Saruwatari;K. Shikano.
international conference on acoustics, speech, and signal processing (2001)

212 Citations

Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks

Yuki Saito;Shinnosuke Takamichi;Hiroshi Saruwatari.
IEEE Transactions on Audio, Speech, and Language Processing (2018)

175 Citations

Speaking-aid systems using GMM-based voice conversion for electrolaryngeal speech

Keigo Nakamura;Tomoki Toda;Hiroshi Saruwatari;Kiyohiro Shikano.
Speech Communication (2012)

172 Citations

Blind Spatial Subtraction Array for Speech Enhancement in Noisy Environment

Y. Takahashi;T. Takatani;K. Osako;H. Saruwatari.
IEEE Transactions on Audio, Speech, and Language Processing (2009)

140 Citations

Maximum Likelihood Voice Conversion Based on GMM with STRAIGHT Mixed Excitation

Yamato Ohtani;Tomoki Toda;Hiroshi Saruwatari;Kiyohiro Shikano.
conference of the international speech communication association (2006)

128 Citations

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Best Scientists Citing Hiroshi Saruwatari

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Chinese University of Hong Kong, Shenzhen

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