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

Job
Senior Researcher
Apr. 2023–Present
The National Institute of Advanced Industrial Science and Technology (AIST), Japan.
Project Research Associate
Sept. 2019–Mar. 2023
Graduate School of Information Science and Technology, The University of Tokyo, Japan.
Researcher
Apr. 2016–Aug. 2019Intelligent Systems Laboratory, SECOM, Japan.
Research Fellow (DC2)
Apr. 2015–Mar. 2016
Japan Society for the Promotion of Science (JSPS), Japan.

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.

SkillsLanguages: English (basic), Japanese (native)
Programming: Python, C/C++, Golang, Javascript, Matlab

Competitive Funds
Funds (Research Representative). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Development of deep-layered analysis-by-synthesis techniques foracoustic scene analysis with human intervention
JSPS KAKENHI
Apr. 2023–Mar. 2027
Sampling-frequency-independent deep learning for audio media processing
JST ACT-X (Frontier of Mathematics and Information Science)
Oct. 2021–Mar. 2024
Research on acoustic scene analysis by integrating time-domain deep learningand multiresolution analysis
JSPS KAKENHI
Apr. 2020–Mar. 2023
Time-domain audio source separation based on wavelet analysis and deep learning
Research Grant (A), The Tateisi Science and Technology Foundation
Apr. 2020–Mar. 2021
Automatic design of wavelet basis functions for end-to-end audio source separation
Kawai Foundation for Sound Technology and Music
Apr. 2020–Mar. 2021
Autonomous audio signal processing based on imitating human auditory system
JSPS KAKENHI
Apr. 2015–Mar. 2016

Funds (Co-researcher). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Exploration of subsurface faults by big data analysis of seismic waveforms
using signal processing and machine learning
Seismology TowArd Research innovation with data of Earthquake
Apr. 2023–Mar. 2026
Research on singing voice synthesis for group singing with humans and computers
JSPS KAKENHI Grant-in-Aid for Challenging Research (Exploratory)
Jul. 2023–Mar. 2025
Development of Machine Learning and Database Infrastracture
for Chorus Singing Voice Synthesis
NII-CRIS Commissioned Research

Sep. 2022–Mar. 2023

Data-Driven Measurement Techniques for High-Resolution Analysis of
Spatial Acoustic Environment and its Applications
JSPS KAKENHI Grant-in-Aid for Scientific Research (B)Apr. 2022–Mar. 2026

Enhancement of Acoustic Virtual Reality and Sound Communication Capability Based on
Small Data Machine Learning Theory
JSPS KAKENHI Grant-in-Aid for Scientific Research (A)Apr. 2020–Mar. 2023

Travel Grants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Grants for Researchers Attending International Conferences
The Tateishi Science and Technology Foundation
Grants for Researchers Attending International Conferences
The Hara Research Foundation
Grants for Researchers Attending International Conferences
The Telecommunications Advancement Foundation

Oct. 2014.
Sept. 2014.
Aug. 2013.

Publications
Journal Papers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Kanami Imamura, Tomohiko Nakamura, Kohei Yatabe, and Hiroshi Saruwatari, “Neural analog filter for
sampling-frequency-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, and Kazunobu Kondo, “PoP-IDLMA: Product-of-prior independent deeply learned matrix
analysis for multichannel music source separation,” IEEE/ACM Transactions on Audio, Speech, andLanguage Processing, vol. 31, pp. 2680–2694, Jul. 2023.
[4] Koichi Saito, Tomohiko Nakamura, Kohei Yatabe, and Hiroshi Saruwatari, “Sampling-frequencyindependent convolutional layer and its application to audio source separation,” IEEE/ACM Transactions
on Audio, Speech, and Language Processing, vol. 30, pp. 2928–2943, Sep. 2022.
[5] Tomohiko Nakamura, Shihori Kozuka, and Hiroshi Saruwatari, “Time-domain audio source separation
with neural networks based on multiresolution analysis,” IEEE/ACM Transactions on Audio, Speech,
and Language Processing, vol. 29, pp. 1687–1701, Apr. 2021.
[6] Tomohiko Nakamura and Hirokazu Kameoka, “Harmonic-temporal factor decomposition for unsupervisedmonaural separation of harmonic sounds,” IEEE/ACM Transactions on Audio, Speech, and Language
Processing, vol. 29, pp. 68–82, Nov. 2020.
[7] Tomohiko Nakamura, Eita Nakamura, and Shigeki Sagayama, “Real-time audio-to-score alignment ofmusic performances containing errors and arbitrary repeats and skips,” IEEE/ACM Transactions on
Audio, Speech, and Language Processing, vol. 24, no. 2, pp. 329–339, Feb. 2016.
[8] Tomohiko Nakamura, Yutaka Hori, and Shinji Hara, “Hierarchical modeling and local stability analysis
for repressilators coupled by quorum sensing,” SICE Journal of Control, Measurement, and System
Integration, vol. 7, no. 3, pp. 133–140, May 2014.
[9] Eita Nakamura, Tomohiko Nakamura, Yasuyuki Saito, Nobutaka Ono, and Shigeki Sagayama, “Outerproduct type hidden Markov model and polyphonic MIDI score following,” Journal of New Music
Research, vol. 43, pp. 183–201, Apr. 2014.

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,” in Proceedings 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 frameworkfor speech extraction based on independent low-rank matrix analysis with spatial regularization andrank-constrained spatial covariance matrix estimation,” in Workshop on Spoken Dialogue Systems for
Cybernetic Avatars (SDS4CA), Sep. 2024.
[5] Kwanghee Choi, Ankita Pasad, Tomohiko Nakamura, Satoru Fukayama, Karen Livescu, and Shinji
Watanabe, “Self-supervised speech representations are more phonetic than semantic,” in Proceedings of
INTERSPEECH, Sep. 2024, pp. 4578–4582.
[6] Yoshiaki Bando, Tomohiko Nakamura, and Shinji Watanabe, “Neural blind source separation anddiarization for distant speech recognition,” in Proceedings of INTERSPEECH, Sep. 2024, pp. 722–726.
[7] Yuto Ishikawa, Kohei Konaka, Tomohiko Nakamura, Norihiro Takamune, and Hiroshi Saruwatari, “Realtime speech extraction using spatially regularized independent low-rank matrix analysis and rankconstrained spatial covariance matrix estimation,” in Proceedings of Hands-Free Speech Communication

and Microphone Arrays, Apr. 2024, pp. 730–734.
[8] Kanami Imamura, Tomohiko Nakamura, Norihiro Takamune, Kohei Yatabe, and Hiroshi Saruwatari,“Algorithms of sampling-frequency-independent layers for non-integer strides,” in Proceedings of European
Signal Processing Conference, Sep. 2023, pp. 326–330.
[9] Joonyong Park, Shinnosuke Takamichi, Tomohiko Nakamura, Kentaro Seki, Detai Xin, and Hiroshi
Saruwatari, “How generative spoken language model encodes noisy speech: Investigation from phonetics
to syntactics,” in Proceedings of INTERSPEECH, Aug. 2023, pp. 1085–1089.
[10] Tomohiko Nakamura, Shinnosuke Takamichi, Naoko Tanji, Satoru Fukayama, and Hiroshi Saruwatari,“jaCappella corpus: A japanese a cappella vocal ensemble corpus,” in Proceedings of IEEE International
Conference on Acoustics, Speech, and Signal Processing, Jun. 2023.
[11] Kota Arai, Yutaro Hirao, Takuji Narumi, Tomohiko Nakamura, Shinnosuke Takamichi, and Shigeo
Yoshida, “TimToShape: Supporting practice of musical instruments by visualizing timbre with 2D shapes
based on crossmodal correspondences,” in Proceedings of ACM Conference on Intelligent User Interfaces,
Mar. 2023, pp. 850–865.
[12] Futa Nakashima, Tomohiko Nakamura, Norihiro Takamune, Satoru Fukayama, and Hiroshi Saruwatari,“Hyperbolic timbre embedding for musical instrument sound synthesis based on variational autoencoders,”
in Proceedings of Asia Pacific Signal and Information Processing Association Annual Summit andConference, Nov. 2022, pp. 736–743.
[13] Yuki Ito, Tomohiko Nakamura, Shoichi Koyama, and Hiroshi Saruwatari, “Head-related transfer function
interpolation from spatially sparse measurements using autoencoder with source position conditioning,”
in Proceedings of International Workshop on Acoustic Signal Enhancement, Sep. 2022.
[14] Kazuhide Shigemi, Shoichi Koyama, Tomohiko Nakamura, and Hiroshi Saruwatari, “Physics-informed
convolutional neural network with bicubic spline interpolation for sound field estimation,” in Proceedingsof International Workshop on Acoustic Signal Enhancement, Sep. 2022.
[15] Takaaki Saeki, Shinnosuke Takamichi, Tomohiko Nakamura, Naoko Tanji, and Hiroshi Saruwatari,“SelfRemaster: Self-supervised speech restoration with analysis-by-synthesis approach using channel
modeling,” in Proceedings of INTERSPEECH, Sep. 2022, pp. 4406–4410.
[16] Masaya Kawamura, Tomohiko Nakamura, Daichi Kitamura, Hiroshi Saruwatari, Yu Takahashi, andKazunobu Kondo, “Differentiable digital signal processing mixture model for synthesis parameter
extraction from mixture of harmonic sounds,” in Proceedings of IEEE International Conference on
Acoustics, Speech, and Signal Processing, May 2022, pp. 941–945.
[17] Takuya Hasumi, Tomohiko Nakamura, Norihiro Takamune, Hiroshi Saruwatari, Daichi Kitamura, Yu
Takahashi, and Kazunobu Kondo, “Multichannel audio source separation with independent deeply
learned matrix analysis using product of source models,” in Proceedings of Asia Pacific Signal andInformation Processing Association Annual Summit and Conference, Dec. 2021, pp. 1226–1233.
[18] Sota Misawa, Norihiro Takamune, Tomohiko Nakamura, Daichi Kitamura, Hiroshi Saruwatari, Masakazu
Une, and Shoji Makino, “Speech enhancement by noise self-supervised rank-constrained spatial covariancematrix estimation via independent deeply learned matrix analysis,” in Proceedings of Asia Pacific Signal
and Information Processing Association Annual Summit and Conference, Dec. 2021, pp. 578–584.
[19] Yusaku Mizobuchi, Daichi Kitamura, Tomohiko Nakamura, Hiroshi Saruwatari, Yu Takahashi, andKazunobu Kondo, “Prior distribution design for music bleeding-sound reduction based on nonnegative
matrix factorization,” in Proceedings of Asia Pacific Signal and Information Processing Association
Annual Summit and Conference, Dec. 2021, pp. 651–658.
[20] Koichi Saito, Tomohiko Nakamura, Kohei Yatabe, Yuma Koizumi, and Hiroshi Saruwatari, “Samplingfrequency-independent audio source separation using convolution layer based on impulse invariant
method,” in Proceedings of European Signal Processing Conference, Aug. 2021, pp. 321–325.
[21] Naoki Narisawa, Rintaro Ikeshita, Norihiro Takamune, Daichi Kitamura, Tomohiko Nakamura, Hiroshi
Saruwatari, and Tomohiro Nakatani, “Independent deeply learned tensor analysis for determined audio
source separation,” in Proceedings of European Signal Processing Conference, Aug. 2021, pp. 326–330.

[22] Takuya Hasumi, Tomohiko Nakamura, Norihiro Takamune, Hiroshi Saruwatari, Daichi Kitamura, Yu
Takahashi, and Kazunobu Kondo, “Empirical bayesian independent deeply learned matrix analysis
for multichannel audio source separation,” in Proceedings of European Signal Processing Conference,
Aug. 2021, pp. 331–335.
[23] Shihori Kozuka, Tomohiko Nakamura, and Hiroshi Saruwatari, “Investigation on wavelet basis functionof DNN-based time domain audio source separation inspired by multiresolution analysis,” in Proceedingsof International Congress and Exposition on Noise Control Engineering, Aug. 2020, pp. 4013–4022.
[24] Tomohiko Nakamura and Hiroshi Saruwatari, “Time-domain audio source separation based on Wave-UNet combined with discrete wavelet transform,” in Proceedings of IEEE International Conference on
Acoustics, Speech, and Signal Processing, May 2020, pp. 386–390.
[25] Tomohiko Nakamura and Hirokazu Kameoka, “Shifted and convolutive source-filter non-negative matrix
factorization for monaural audio source separation,” in Proceedings of IEEE International Conference on
Acoustics, Speech, and Signal Processing, Mar. 2016, pp. 489–493.
[26] Tomohiko Nakamura and Hirokazu Kameoka, “Lp-norm non-negative matrix factorization and its
application to singing voice enhancement,” in Proceedings of IEEE International Conference on Acoustics,
Speech, and Signal Processing, Apr. 2015, pp. 2115–2119.
[27] Tomohiko Nakamura, Kotaro Shikata, Norihiro Takamune, and Hirokazu Kameoka, “Harmonic-temporalfactor decomposition incorporating music prior information for informed monaural source separation,” inProceedings of International Society for Music Information Retrieval Conference, Oct. 2014, pp. 623–628.
[28] Tomohiko Nakamura and Hirokazu Kameoka, “Fast signal reconstruction from magnitude spectrogramof continuous wavelet transform based on spectrogram consistency,” in Proceedings of International
Conference on Digital Audio Effects, Sep. 2014, pp. 129–135.
[29] Takuya Higuchi, Hirofumi Takeda, Tomohiko Nakamura, and Hirokazu Kameoka, “A unified approachfor underdetermined blind signal separation and source activity detection by multichannel factorial hidden
Markov models,” in Proceedings of INTERSPEECH, Sep. 2014, pp. 850–854.
[30] Tomohiko Nakamura, Hirokazu Kameoka, Kazuyoshi Yoshii, and Masataka Goto, “Timbre replacementof harmonic and drum components for music audio signals,” in Proceedings of IEEE International
Conference on Acoustics, Speech, and Signal Processing, May 2014, pp. 7520–7524.
[31] Takuya Higuchi, Norihiro Takamune, Tomohiko Nakamura, and Hirokazu Kameoka, “Underdeterminedblind separation and tracking of moving sources based on DOA-HMM,” in Proceedings of IEEE
International Conference on Acoustics, Speech, and Signal Processing, May 2014, pp. 3215–3219.
[32] Shigeki Sagayama, Tomohiko Nakamura, Eita Nakamura, Yasuyuki Saito, Hirokazu Kameoka, andNobutaka Ono, “Automatic music accompaniment allowing errors and arbitrary repeats and jumps,” inProceedings of Meetings on Acoustics, Acoustic Society of America, May 2014, vol. 21, 35003.
[33] Tomohiko Nakamura, Eita Nakamura, and Shigeki Sagayama, “Acoustic score following to musicalperformance with errors and arbitrary repeats and skips for automatic accompaniment,” in Proceedingsof Sound and Music Computing Conference, Aug. 2013, pp. 299–304.
[34] Masahiro Nakano, Jonathan Le Roux, Hirokazu Kameoka, Tomohiko Nakamura, Nobutaka Ono, andShigeki Sagayama, “Bayesian nonparametric spectrogram modeling based on infinite factorial infinite
hidden Markov model,” in Proceedings of IEEE Workshop on Applications of Signal Processing to Audioand Acoustics, Oct. 2011, pp. 325–328.
[35] Tomohiko Nakamura, Shinji Hara, and Yutaka Hori, “Local stability analysis for a class of quorumsensing networks with cyclic gene regulatory networks,” in Proceedings of SICE Annual Conference,
Sep. 2011, pp. 2111–2116.

Invited Talks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Daichi Kitamura, Tomohiko Nakamura, “Fundamentals and Applications of Audio Source Separation —A Guide to Becoming 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, “Physicsinformed machine learning 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] Tomohiko Nakamura, “Trained model, training device, training method, and training program,” Japan
Patent JP7304235, 28-Jun-2023.
[4] Tomohiko Nakamura, Syohei Kunimatsu, Toshihiko Sakurai, and Ittoku Ohnishi, “Camera placement
evaluation device, method, and program,” Japan Patent JP7291013, 06-Jun-2023. (in Japanse)[5] Rintaro Ikeshita, Tomohiro Nakatani, Naoki Narisawa, Norihiro Takamune, Tomohiko Nakamura, andHiroshi Saruwatari, “Signal processing device, method, and program,” Japan Unexamined Patent
JP2023-089431, 16-Dec-2021.
[6] Tomohiko Nakamura, “Object recognition device, method, and program,” Japan Patent JP6773829,
05-Oct-2020. (in Japanse)[7] Tomohiko Nakamura, “Training device, method, and program for object recognition, and object recognition device,” Japan Patent JP6773825, 05-Oct-2020. (in Japanse)[8] Tomohiko Nakamura, Tadahiko Ito, and Masaki Shimaoka, “Certificate management device,” Japan
Patent JP6647259, 05-Oct-2020. (in Japanse)[9] Tomohiko Nakamura, “Database integration device, method, and program, and data imputation device,”
Japan Patent JP6768101, 24-Sep-2020. (in Japanse)[10] Tomohiko Nakamura and Hirokazu Kameoka, “Vocal tract spectrum estimation device, method, andprogram,” Japan Patent JP6420781, 19-Oct-2018. (in Japanse)

Ph.D. Thesis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
[1] Tomohiko Nakamura, “Source-Filter Representation and Phase Estimation in Continuous Wavelet
Transform Domain for Monaural Music Audio Editing,” Ph.D. Thesis, The University of Tokyo, Mar.
2016.

Awards
1. The Awaya Kiyoshi Research Award, ASJ, Mar. 2024.
2. The Itakura Prize Innovative Young Researcher Award, ASJ, Mar. 2022.
3. 2021 Encouragement Award, Foundation of the Promotion of Engineering Research, Jul. 2021.
4. 2021 Otogaku Symposium Best Presentation Award, Jun. 2021.
5. IPSJ Recommended Ph.D. Thesis, Aug. 2016.
6. Dean’s Award of Graduate School of Information Science and Technology, The University of Tokyo,
Mar. 2016.
7. IPSJ Yamashita SIG Research Award, Mar. 2016.
8. SICE Best Paper Award (Takeda Award), Oct. 2015.
9. 2015 Otogaku Symposium Award, May 2015.

10. Best Student Presentation Award from ASJ, Mar. 2014.
11. IPSJ Certificate of Excellent Master’s Thesis, Mar. 2013.
12. Student Encouragement Award of IPSJ National Convention, Mar. 2013.
13. SICE Annual Conference 2011 International Award, Sept. 2011.
14. SICE Annual Conference 2011 Finalist of Young Author Award, Sept. 2011.

Academic Activities
Jun. 2023–Jun. 2027: Associate Editor, the IEICE Transactions on Information and Systems (Japanese
Edition)Jun. 2023–Jun. 2025: Committee Member, the IEICE Technical Group on Engineering Acoustics
Apr. 2023–Jun. 2025: Secretary, the IPSJ Special Interest Group on Music and Computer
Mar. 2023–Nov. 2023: Program committee, the International Symposium on Computer Music Multidisciplinary Research 2023
Nov. 2021–Mar. 2027: Secretary, the ASJ Student and Young Researchers Forum
Apr. 2017–Mar. 2021: Committee Member, the IPSJ Special Interest Group on Music and Computer