Naoyuki Kamo

536 total citations
19 papers, 341 citations indexed

About

Naoyuki Kamo is a scholar working on Signal Processing, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Naoyuki Kamo has authored 19 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Signal Processing, 12 papers in Artificial Intelligence and 5 papers in Computational Mechanics. Recurrent topics in Naoyuki Kamo's work include Speech and Audio Processing (17 papers), Speech Recognition and Synthesis (12 papers) and Music and Audio Processing (5 papers). Naoyuki Kamo is often cited by papers focused on Speech and Audio Processing (17 papers), Speech Recognition and Synthesis (12 papers) and Music and Audio Processing (5 papers). Naoyuki Kamo collaborates with scholars based in Japan, China and United States. Naoyuki Kamo's co-authors include Wangyou Zhang, Shinji Watanabe, Tomohiro Nakatani, Tomoki Hayashi, Chenda Li, Jing Shi, Xuankai Chang, Hirofumi Inaguma, Pengcheng Guo and Yosuke Higuchi and has published in prestigious journals such as IEEE Signal Processing Letters, IEEE/ACM Transactions on Audio Speech and Language Processing and Computer Speech & Language.

In The Last Decade

Naoyuki Kamo

16 papers receiving 328 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Naoyuki Kamo Japan 10 257 255 48 24 16 19 341
Aswin Shanmugam Subramanian United States 11 291 1.1× 264 1.0× 42 0.9× 15 0.6× 17 1.1× 23 355
Chenda Li China 9 297 1.2× 313 1.2× 33 0.7× 16 0.7× 24 1.5× 24 390
Simon Welker Germany 8 255 1.0× 193 0.8× 57 1.2× 17 0.7× 34 2.1× 21 322
Xiaojia Zhao United States 7 358 1.4× 287 1.1× 39 0.8× 24 1.0× 34 2.1× 8 395
Cheng Yu Taiwan 6 333 1.3× 232 0.9× 77 1.6× 49 2.0× 34 2.1× 12 372
Michael Wohlmayr Austria 7 241 0.9× 166 0.7× 71 1.5× 28 1.2× 29 1.8× 17 303
Chang Huai You Singapore 11 251 1.0× 214 0.8× 69 1.4× 27 1.1× 48 3.0× 30 323
Miquel Espi Japan 5 268 1.0× 158 0.6× 70 1.5× 25 1.0× 28 1.8× 13 295
Zhaoheng Ni United States 11 177 0.7× 165 0.6× 40 0.8× 52 2.2× 28 1.8× 26 290

Countries citing papers authored by Naoyuki Kamo

Since Specialization
Citations

This map shows the geographic impact of Naoyuki Kamo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Naoyuki Kamo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Naoyuki Kamo more than expected).

Fields of papers citing papers by Naoyuki Kamo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Naoyuki Kamo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Naoyuki Kamo. The network helps show where Naoyuki Kamo may publish in the future.

Co-authorship network of co-authors of Naoyuki Kamo

This figure shows the co-authorship network connecting the top 25 collaborators of Naoyuki Kamo. A scholar is included among the top collaborators of Naoyuki Kamo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Naoyuki Kamo. Naoyuki Kamo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Kamo, Naoyuki, Naohiro Tawara, Hiroshi Satō, et al.. (2025). Microphone array geometry-independent multi-talker distant ASR: NTT system for DASR task of the CHiME-8 challenge. Computer Speech & Language. 95. 101820–101820.
2.
Kamo, Naoyuki, et al.. (2024). Ensemble Inference for Diffusion Model-Based Speech Enhancement. 735–739. 2 indexed citations
3.
Nakatani, Tomohiro, Naoyuki Kamo, Marc Delcroix, & Shoko Araki. (2024). Multi-Stream Diffusion Model for Probabilistic Integration of Model-Based and Data-Driven Speech Enhancement. 65–69.
4.
Nakatani, Tomohiro, et al.. (2024). Diffusion Model-Based MIMO Speech Denoising and Dereverberation. 455–459. 1 indexed citations
5.
Kamo, Naoyuki, Naohiro Tawara, Hiroshi Satō, et al.. (2024). NTT Multi-Speaker ASR System for the DASR Task of CHiME-8 Challenge. SPIRE - Sciences Po Institutional REpository. 69–74. 2 indexed citations
6.
Kamo, Naoyuki, Marc Delcroix, & Tomohiro Nakatani. (2023). Target Speech Extraction with Conditional Diffusion Model. 176–180. 11 indexed citations
7.
Kamo, Naoyuki, Naohiro Tawara, Kohei Matsuura, et al.. (2023). NTT Multi-Speaker ASR System for the DASR Task of CHiME-7 Challenge. 45–50. 4 indexed citations
8.
Sato, Hiroshi, Tsubasa Ochiai, Marc Delcroix, et al.. (2022). Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 6287–6291. 13 indexed citations
9.
Nakatani, Tomohiro, Rintaro Ikeshita, Naoyuki Kamo, et al.. (2022). AI Hears Your Voice as if It Were Right Next to You—Audio Processing Framework for Separating Distant Sounds with Close-microphone Quality. NTT technical review. 20(10). 49–55.
10.
Nakatani, Tomohiro, Rintaro Ikeshita, Keisuke Kinoshita, et al.. (2022). Switching Independent Vector Analysis and its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms. IEEE/ACM Transactions on Audio Speech and Language Processing. 30. 1032–1047. 9 indexed citations
11.
Kamo, Naoyuki, Rintaro Ikeshita, Keisuke Kinoshita, & Tomohiro Nakatani. (2022). Importance of Switch Optimization Criterion in Switching WPE Dereverberation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 30. 176–180. 3 indexed citations
12.
Zhang, Wangyou, Christoph Boeddeker, Shinji Watanabe, et al.. (2021). End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend. arXiv (Cornell University). 23 indexed citations
13.
Ikeshita, Rintaro, Naoyuki Kamo, & Tomohiro Nakatani. (2021). Blind Signal Dereverberation Based on Mixture of Weighted Prediction Error Models. IEEE Signal Processing Letters. 28. 399–403. 18 indexed citations
14.
Satō, Hiroshi, Tsubasa Ochiai, Marc Delcroix, et al.. (2021). Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition. arXiv (Cornell University). 13 indexed citations
15.
Nakatani, Tomohiro, Rintaro Ikeshita, Naoyuki Kamo, et al.. (2021). Switching Convolutional Beamformer. 2021 29th European Signal Processing Conference (EUSIPCO). 17. 266–270. 2 indexed citations
16.
Ikeshita, Rintaro, Keisuke Kinoshita, Naoyuki Kamo, & Tomohiro Nakatani. (2021). Online Speech Dereverberation Using Mixture of Multichannel Linear Prediction Models. IEEE Signal Processing Letters. 28. 1580–1584. 9 indexed citations
17.
Guo, Pengcheng, Xuankai Chang, Tomoki Hayashi, et al.. (2021). Recent Developments on Espnet Toolkit Boosted By Conformer. 5874–5878. 149 indexed citations
18.
Watanabe, Shinji, Xuankai Chang, Pengcheng Guo, et al.. (2021). The 2020 ESPnet Update: New Features, Broadened Applications, Performance Improvements, and Future Plans. 30. 1–6. 29 indexed citations
19.
Li, Chenda, Jing Shi, Wangyou Zhang, et al.. (2020). ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration. arXiv (Cornell University). 53 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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