Shoko Araki

7.7k total citations
219 papers, 4.7k citations indexed

About

Shoko Araki is a scholar working on Signal Processing, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Shoko Araki has authored 219 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 204 papers in Signal Processing, 105 papers in Computational Mechanics and 64 papers in Artificial Intelligence. Recurrent topics in Shoko Araki's work include Speech and Audio Processing (200 papers), Blind Source Separation Techniques (117 papers) and Advanced Adaptive Filtering Techniques (104 papers). Shoko Araki is often cited by papers focused on Speech and Audio Processing (200 papers), Blind Source Separation Techniques (117 papers) and Advanced Adaptive Filtering Techniques (104 papers). Shoko Araki collaborates with scholars based in Japan, United States and Germany. Shoko Araki's co-authors include Shoji Makino, Hiroshi Sawada, Ryo Mukai, Tomohiro Nakatani, Marc Delcroix, Keisuke Kinoshita, Nobutaka Ito, Hiroshi Saruwatari, Tsuyoki Nishikawa and Masakiyo Fujimoto and has published in prestigious journals such as IEEE Transactions on Signal Processing, The Journal of the Acoustical Society of America and Electronics Letters.

In The Last Decade

Shoko Araki

203 papers receiving 4.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shoko Araki Japan 35 4.3k 2.3k 1.2k 330 225 219 4.7k
Guy J. Brown United Kingdom 29 2.5k 0.6× 710 0.3× 843 0.7× 1.1k 3.4× 475 2.1× 120 3.3k
Hideki Asoh Japan 21 543 0.1× 194 0.1× 548 0.5× 181 0.5× 377 1.7× 103 1.5k
Takuya Yoshioka Japan 33 4.7k 1.1× 1.4k 0.6× 3.3k 2.8× 542 1.6× 325 1.4× 150 5.3k
Douglas O’Shaughnessy Canada 26 2.2k 0.5× 262 0.1× 2.2k 1.9× 255 0.8× 541 2.4× 258 3.4k
Shigeki Sagayama Japan 28 2.3k 0.5× 497 0.2× 1.1k 1.0× 243 0.7× 1.0k 4.4× 243 3.0k
Tatsuya Kawahara Japan 29 1.7k 0.4× 226 0.1× 3.3k 2.8× 142 0.4× 454 2.0× 410 4.4k
J.R. Deller United States 16 1.9k 0.4× 571 0.2× 1.4k 1.2× 126 0.4× 543 2.4× 102 2.9k
Maurizio Omologo Italy 24 1.8k 0.4× 601 0.3× 889 0.8× 126 0.4× 292 1.3× 119 2.2k
Xavier Serra Spain 32 3.5k 0.8× 168 0.1× 813 0.7× 946 2.9× 2.5k 11.2× 235 4.2k
Nikos Fakotakis Greece 25 862 0.2× 83 0.0× 1.5k 1.2× 66 0.2× 716 3.2× 170 2.6k

Countries citing papers authored by Shoko Araki

Since Specialization
Citations

This map shows the geographic impact of Shoko Araki'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 Shoko Araki with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shoko Araki more than expected).

Fields of papers citing papers by Shoko Araki

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shoko Araki. 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 Shoko Araki. The network helps show where Shoko Araki may publish in the future.

Co-authorship network of co-authors of Shoko Araki

This figure shows the co-authorship network connecting the top 25 collaborators of Shoko Araki. A scholar is included among the top collaborators of Shoko Araki 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 Shoko Araki. Shoko Araki is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Delcroix, Marc, et al.. (2025). SoundBeam meets M2D: Target Sound Extraction with Audio Foundation Model. 1–5. 2 indexed citations
2.
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.
3.
Nakatani, Tomohiro, et al.. (2024). DOA-informed switching independent vector extraction and beamforming for speech enhancement in underdetermined situations. EURASIP Journal on Audio Speech and Music Processing. 2024(1).
4.
Kamo, Naoyuki, et al.. (2024). Ensemble Inference for Diffusion Model-Based Speech Enhancement. 735–739. 2 indexed citations
5.
Delcroix, Marc, et al.. (2024). Probing Self-Supervised Learning Models With Target Speech Extraction. 535–539. 1 indexed citations
6.
Nakatani, Tomohiro, et al.. (2024). Blind and Spatially-Regularized Online Joint Optimization of Source Separation, Dereverberation, and Noise Reduction. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 1157–1172. 5 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.
Delcroix, Marc, et al.. (2022). SoundBeam: Target Sound Extraction Conditioned on Sound-Class Labels and Enrollment Clues for Increased Performance and Continuous Learning. IEEE/ACM Transactions on Audio Speech and Language Processing. 31. 121–136. 21 indexed citations
9.
Nakatani, Tomohiro, Keisuke Kinoshita, Rintaro Ikeshita, Hiroshi Sawada, & Shoko Araki. (2019). Simultaneous Denoising, Dereverberation, and Source Separation Using a Unified Convolutional Beamformer. 1. 224–228. 2 indexed citations
10.
11.
Araki, Shoko, Masakiyo Fujimoto, Takuya Yoshioka, et al.. (2015). Deep Learning Based Distant-talking Speech Processing in Real-world Sound Environments. NTT technical review. 13(11). 19–24. 2 indexed citations
12.
Kato, Hiroko, et al.. (2006). Parametric-Pearson-based independent component analysis for frequency-domain blind speech separation. European Signal Processing Conference. 1–5. 1 indexed citations
13.
Araki, Shoko, Hiroshi Sawada, Ryo Mukai, & Shoji Makino. (2005). A novel blind source separation method with observation vector clustering. 25 indexed citations
14.
Winter, Stefan, Hiroshi Sawada, Shoko Araki, & Shoji Makino. (2004). Hierarchical clustering applied to overcomplete BSS for convolutive mixtures. Conference of the International Speech Communication Association. 48. 2 indexed citations
15.
Mukai, Ryo, Hiroshi Sawada, Shoko Araki, & Shoji Makino. (2004). Blind Source Separation for MOving Speech Signals Using Blockwise ICA and Residual Crosstalk Subtraction. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences. 87(8). 1941–1948. 26 indexed citations
16.
Mukai, Ryo, Hiroshi Sawada, Shoko Araki, & Shoji Makino. (2004). Robust real-time blind source separation for moving speakers in a room. 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).. 5. V–469. 30 indexed citations
17.
Aichner, Robert, Herbert Buchner, Shoko Araki, & Shoji Makino. (2003). ON-LINE TIME-DOMAIN BLIND SOURCE SEPARATION OF NONSTATIONARY CONVOLVED SIGNALS. 32(8). 814–816. 17 indexed citations
18.
Araki, Shoko, et al.. (2003). Blind Separation of More Speech than Sensors with Less Distortion by Combining Sparseness and ICA. 23(3). 341–55. 15 indexed citations
19.
Sawada, Hiroshi, Ryo Mukai, Shoko Araki, & Shoji Makino. (2001). A POLAR-COORDINATE BASED ACTIVATION FUNCTION FOR FREQUENCY DOMAIN BLIND SOURCE SEPARATION. 65(3). 197–203. 24 indexed citations
20.
Mukai, Ryo, Shoko Araki, & Shoji Makino. (2001). Separation And Dereverberation Performance Of Frequency Domain Blind Source Separation. 5 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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