2026 Shigeki Sagayama: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	42	8476	8238	115	109	351	6541

Overview

Shigeki Sagayama is affiliated with the University of Tokyo in Japan and has contributed to research across several interdisciplinary fields including Computer Science, Neuroscience, and Psychology. Their research work spans multiple subfields such as Cognitive Neuroscience, Signal Processing, Developmental and Educational Psychology, Computer Vision and Pattern Recognition, and Education.

The scientist's main research topics cover Autism Spectrum Disorder Research, Behavioral and Psychological Studies, Music Technology and Sound Studies, Music and Audio Processing, Child Development and Digital Technology, Neuroscience and Music Perception, and Tactile and Sensory Interactions.

Recent papers authored or co-authored by Sagayama include:

Objective assessment of autism spectrum disorder based on performance in structured interpersonal acting-out tasks with prosodic stability and variability (2023, Autism Research)
A Parameterized Harmony Model for Automatic Music Completion (2020, Journal of Information Processing)
Music Recreation in Nursing Home using Automatic Music Accompaniment System and Score of VLN (2020, 2020 IEEE 2nd Global Conference on Life Sciences and Technologies [LifeTech])
DNN-Based Full-Band Speech Synthesis Using GMM Approximation of Spectral Envelope (2020, IEICE Transactions on Information and Systems)
Use of Nods Less Synchronized with Turn-Taking and Prosody During Conversations in Adults with Autism (2022, Interspeech 2022)

Sagayama frequently collaborates with other researchers, including Keiko Ochi, Nobutaka Ono, Miho Kuroda, Keiho Owada, and Hidenori Yamasue. Collaborative work spans multiple projects and publications, indicating a research network focused primarily on autism studies, speech, and music-related technologies.

The scientist publishes in diverse venues reflecting their multidisciplinary focus. These include:

Autism Research
Journal of Information Processing
2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech)
IEICE Transactions on Information and Systems
Interspeech 2022

Shigeki Sagayama's work integrates approaches from computational modeling, signal processing, and neuropsychological assessment methods. The research covers applications such as automatic music completion, speech synthesis, and the behavioral analysis of autism spectrum disorder, particularly in interpersonal and conversational settings.

Best Publications

Dynamic Time-Alignment Kernel in Support Vector Machine

Hiroshi Shimodaira;Ken-ichi Noma;Mitsuru Nakai;Shigeki Sagayama

294
Citations
A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering

H. Kameoka;T. Nishimoto;S. Sagayama

231
Citations
A successive state splitting algorithm for efficient allophone modeling

J. Takami;S. Sagayama

230
Citations
Complex NMF: A new sparse representation for acoustic signals

Hirokazu Kameoka;Nobutaka Ono;Kunio Kashino;Shigeki Sagayama

209
Citations
Separation of a monaural audio signal into harmonic/percussive components by complementary diffusion on spectrogram

Nobutaka Ono;Kenichi Miyamoto;Jonathan Le Roux;Hirokazu Kameoka

195
Citations
Free software toolkit for Japanese large vocabulary continuous speech recognition

Tatsuya Kawahara;Akinobu Lee;Tetsunori Kobayashi;Kazuya Takeda

161
Citations
Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with β-divergence

Masahiro Nakano;Hirokazu Kameoka;Jonathan Le Roux;Yu Kitano

157
Citations
Substroke approach to HMM-based on-line Kanji handwriting recognition

M. Nakai;N. Akira;H. Shimodaira;S. Sagayama

147
Citations
Multipitch Analysis with Harmonic Nonnegative Matrix Approximation.

Stanislaw Andrzej Raczynski;Nobutaka Ono;Shigeki Sagayama

140
Citations
Blind alignment of asynchronously recorded signals for distributed microphone array

Nobutaka Ono;Hitoshi Kohno;Nobutaka Ito;Shigeki Sagayama

140
Citations
A REAL-TIME EQUALIZER OF HARMONIC AND PERCUSSIVE COMPONENTS IN MUSIC SIGNALS

Nobutaka Ono;Kenichi Miyamoto;Hirokazu Kameoka;Shigeki Sagayama

138
Citations
Fast signal reconstruction from magnitude STFT spectrogram based on spectrogram consistency

Jonathan Le Roux;Hirokazu Kameoka;Nobutaka Ono;Shigeki Sagayama

120
Citations
HMM-based approach for automatic chord detection using refined acoustic features

Yushi Ueda;Yuki Uchiyama;Takuya Nishimoto;Nobutaka Ono

119
Citations
Explicit consistency constraints for STFT spectrograms and their application to phase reconstruction.

Jonathan Le Roux;Nobutaka Ono;Shigeki Sagayama

119
Citations
Jacobian approach to fast acoustic model adaptation

S. Sagayama;Y. Yamaguchi;S. Takahashi;J. Takahashi

118
Citations
Sparseness-Based 2CH BSS using the EM Algorithm in Reverberant Environment

Yosuke Izumi;Nobutaka Ono;Shigeki Sagayama

110
Citations
Introduction to the Special Section on Deep Learning for Speech and Language Processing

Dong Yu;G. Hinton;N. Morgan;Jen-Tzung Chien

104
Citations
Multiple-regression hidden Markov model

K. Fujinaga;M. Nakai;H. Shimodaira;S. Sagayama

96
Citations
Melody line estimation in homophonic music audio signals based on temporal-variability of melodic source

Hideyuki Tachibana;Takuma Ono;Nobutaka Ono;Shigeki Sagayama

96
Citations
Tree-structured speaker clustering for fast speaker adaptation

T. Kosaka;S. Sagayama

94
Citations

Frequent Co-Authors

Nobutaka Ono Tokyo Metropolitan University

Hirokazu Kameoka NTT (Japan)

Emmanuel Vincent University of Lorraine

Jonathan Le Roux Mitsubishi Electric (United States)

Satoshi Nakamura Nara Institute of Science and Technology

Takao Kobayashi Tokyo Institute of Technology

Keiichi Tokuda Nagoya Institute of Technology

Kiyohiro Shikano Nara Institute of Science and Technology

Alain de Cheveigné École Normale Supérieure

Rémi Gribonval École Normale Supérieure de Lyon

External Links

Personal website of Shigeki Sagayama

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