Tomohiko Nakamura
2-4-7 Aomi, Koto-ku, Tokyo, Japan 135-0064
 https://tomohikonakamura.github.io/Tomohiko-Nakamura/# tomohiko.nakamura.jp@ieee.org§ https://github.com/TomohikoNakamura

Research Interests
Signal-processing-inspired deep learning, audio and music signal processing, and machine learning

JobSenior Researcher
The National Institute of Advanced Industrial Science and Technology (AIST), Japan.
Project Research Associate
Graduate School of Information Science and Technology, The University of Tokyo, Japan.
Researcher
Intelligent Systems Laboratory, SECOM, Japan.
Research Fellow (DC2)Japan Society for the Promotion of Science (JSPS), Japan.

Apr. 2023–PresentSept. 2019–Mar. 2023
Apr. 2016–Aug. 2019
Apr. 2015–Mar. 2016

Education
Ph.D. degree in Information Science and Technology
Graduate School of Information Science and Technology, The University of Tokyo, Japan.
Master’s degree in Information Science and Technology
Graduate School of Information Science and Technology, The University of Tokyo, Japan.
Bachelor’s degree in Engineering
Faculty of Engineering, The University of Tokyo, Japan.

Mar. 2016
Mar. 2013
Mar. 2011

Teaching
Applied Gaussian Process and Machine Learning
6, Dec. 2021
Graduate School of Information Science and Technology, The University of Tokyo, Japan.
Advanced Signal Processing
23, June 2020 and 21, June 2022
Graduate School of Information Science and Technology, The University of Tokyo, Japan.
Student ExperimentApr. 2020–Mar. 2023
Department of Mathematical engineering and information physics, The University of Tokyo, Japan.

Languages
Japanese (native), English (basic)

Competitive Funds
Development of deep-layered analysis-by-synthesis techniques foracoustic scene analysis with human intervention
JSPS KAKENHI
Sampling-frequency-independent deep learning for audio media processing
JST ACT-X (Frontier of Mathematics and Information Science)
Research on acoustic scene analysis by integrating time-domain deep learningand multiresolution analysis
JSPS KAKENHI+ 3 funds as representative and 5 funds as co-researcher.

Apr. 2023–Mar. 2027
Oct. 2021–Mar. 2024

Apr. 2020–Mar. 2023

Publications
Journal Papers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Kanami Imamura, Tomohiko Nakamura, Kohei Yatabe, and Hiroshi Saruwatari, “Neural analog filter for samplingfrequency-independent convolutional layer,” APSIPA Transactions on Signal and Information Processing, Dec. 2024.
[2] Takaaki Saeki, Shinnosuke Takamichi, Tomohiko Nakamura, Naoko Tanji, and Hiroshi Saruwatari, “SelfRemaster:Self-supervised speech restoration for historical audio resources,” IEEE Access, vol. 11, pp. 144831–144843, Jan. 2024.
[3] Takuya Hasumi, Tomohiko Nakamura, Norihiro Takamune, Hiroshi Saruwatari, Daichi Kitamura, Yu Takahashi, andKazunobu Kondo, “PoP-IDLMA: Product-of-prior independent deeply learned matrix analysis for multichannel musicsource separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2680–2694,Jul. 2023.
[4] Koichi Saito, Tomohiko Nakamura, Kohei Yatabe, and Hiroshi Saruwatari, “Sampling-frequency-independent convolutional layer and its application to audio source separation,” IEEE/ACM Transactions on Audio, Speech, andLanguage Processing, vol. 30, pp. 2928–2943, Sep. 2022.
[5] +5 papers

Peer-Reviewed International Conferences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Yuto Ishikawa, Osamu Take, Tomohiko Nakamura, Norihiro Takamune, Yuki Saito, Shinnosuke Takamichi, and Hiroshi
Saruwatari, “Real-time noise estimation for lombard-effect speech synthesis in human–avatar dialogue systems,” inProceedings of Asia Pacific Signal and Information Processing Association Annual Summit and Conference, Dec.
2024.
[2] Hiroaki Hyodo, Shinnosuke Takamichi, Tomohiko Nakamura, Junya Koguchi, and Hiroshi Saruwatari, “DNN-based
ensemble singing voice synthesis with interactions between singers,” in Proceedings of IEEE Spoken Language
Technology Workshop, Dec. 2024.
[3] Hitoshi Suda, Shunsuke Yoshida, Tomohiko Nakamura, Fukayama Satoru, and Jun Ogata, “FruitsMusic: A real-world
corpus of Japanese idol-group songs,” in Proceedings of International Society for Music Information Retrieval
Conference, Nov. 2024.
[4] Yuto Ishikawa, Tomohiko Nakamura, Norihiro Takamune, and Hiroshi Saruwatari, “Real-time framework for speech
extraction based on independent low-rank matrix analysis with spatial regularization and rank-constrained spatial
covariance matrix estimation,” in Workshop on Spoken Dialogue Systems for Cybernetic Avatars (SDS4CA), Sep. 2024.
[5] +31 papers

Invited Talks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Daichi Kitamura, Tomohiko Nakamura, “Fundamentals and Applications of Audio Source Separation — A Guide toBecoming an Expert,” 2023 Otogaku Symposium, Jun. 2023. (in Japanese)[2] Tomohiko Nakamura, “Signal-processing-inspired deep learning,” IEEE NZ Signal Processing/Information Theory
Joint Chapter in co-hosted by the Acoustics Research Centre, University of Auckland, Dec. 2022.
[3] Tomohiko Nakamura, “Audio source separation combining wavelet transform and deep neural network,” Meeting on
Technical Committee on Engineering Acoustics, IEICE, Nov. 2022. (in Japanese)

Overview Papers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Shoichi Koyama, Juliano Ribeiro, Tomohiko Nakamura, Natsuki Ueno, and Mirco Pezzoli, “Physics-informed machinelearning for sound field estimation,” Special Issue on Model-Based and Data-Driven Audio Signal Processing, IEEE
Signal Processing Magazine, 2024.
[2] Hirokazu Kameoka, Tomohiko Nakamura, and Norihiro Takamune, “Recent Advances in Music Signal Processing
Techniques,” The Journal of Institute of Electronics, Information and Communication Engineers, vol. 98, no. 6, pp.
467–474, Jun. 2015. (in Japanese)

Patents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Tomohiko Nakamura, “Object recognition device, method, and program,” Japan Patent JP7349288, 13-Sep-2023.
[2] Tomohiko Nakamura, “Object recognition device, method, and program,” Japan Patent JP7349290, 13-Sep-2023.
[3] +8 patentsAwards
1. The Awaya Kiyoshi Research Award, ASJ, Mar. 2024.
2. The Itakura Prize Innovative Young Researcher Award, ASJ, Mar. 2022.
3. Dean’s Award of Graduate School of Information Science and Technology, The University of Tokyo, Mar. 2016.

4. +11 awards