Takuya Higuchi

1.4k total citations
37 papers, 875 citations indexed

About

Takuya Higuchi is a scholar working on Signal Processing, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Takuya Higuchi has authored 37 papers receiving a total of 875 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Signal Processing, 26 papers in Artificial Intelligence and 6 papers in Computational Mechanics. Recurrent topics in Takuya Higuchi's work include Speech and Audio Processing (26 papers), Speech Recognition and Synthesis (20 papers) and Music and Audio Processing (12 papers). Takuya Higuchi is often cited by papers focused on Speech and Audio Processing (26 papers), Speech Recognition and Synthesis (20 papers) and Music and Audio Processing (12 papers). Takuya Higuchi collaborates with scholars based in Japan, United States and United Kingdom. Takuya Higuchi's co-authors include Tomohiro Nakatani, Nobutaka Ito, Takuya Yoshioka, Keisuke Kinoshita, Marc Delcroix, Shoko Araki, Atsunori Ogawa, Kateřina Žmolíková, Masahiro Murakawa and Masaya Iwata and has published in prestigious journals such as IEEE Journal of Solid-State Circuits, Computer and IEEE/ACM Transactions on Audio Speech and Language Processing.

In The Last Decade

Takuya Higuchi

36 papers receiving 805 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Takuya Higuchi Japan 15 674 516 276 75 59 37 875
Hossein Sameti Iran 14 663 1.0× 577 1.1× 199 0.7× 14 0.2× 48 0.8× 103 848
Liang Lu United States 19 851 1.3× 932 1.8× 84 0.3× 23 0.3× 38 0.6× 64 1.1k
Richard M. Dansereau Canada 11 305 0.5× 133 0.3× 149 0.5× 62 0.8× 46 0.8× 81 545
B. Raj United States 16 843 1.3× 683 1.3× 181 0.7× 36 0.5× 25 0.4× 29 953
Takehiro Moriya Japan 16 604 0.9× 129 0.3× 178 0.6× 158 2.1× 30 0.5× 146 902
S.V. Andersen Denmark 10 341 0.5× 134 0.3× 154 0.6× 66 0.9× 22 0.4× 42 518
Aditya Arie Nugraha Japan 10 617 0.9× 301 0.6× 206 0.7× 22 0.3× 46 0.8× 31 676
Axel Plinge Germany 12 242 0.4× 118 0.2× 81 0.3× 107 1.4× 54 0.9× 33 376
Marilyn L. Malpass United States 5 564 0.8× 192 0.4× 413 1.5× 94 1.3× 123 2.1× 10 714
Wai C. Chu United States 8 284 0.4× 141 0.3× 101 0.4× 57 0.8× 14 0.2× 24 642

Countries citing papers authored by Takuya Higuchi

Since Specialization
Citations

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

Fields of papers citing papers by Takuya Higuchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takuya Higuchi

This figure shows the co-authorship network connecting the top 25 collaborators of Takuya Higuchi. A scholar is included among the top collaborators of Takuya Higuchi 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 Takuya Higuchi. Takuya Higuchi 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.
Higuchi, Takuya, et al.. (2024). Multichannel Voice Trigger Detection Based on Transform-Average-Concatenate. 510–514. 1 indexed citations
2.
Higuchi, Takuya, Jee-weon Jung, Tatiana Likhomanenko, et al.. (2024). Can you Remove the Downstream Model for Speaker Recognition with Self-Supervised Speech Features?. 4648–4652. 2 indexed citations
3.
Higuchi, Takuya, et al.. (2021). Dynamic Curriculum Learning via Data Parameters for Noise Robust Keyword Spotting. 6848–6852. 5 indexed citations
4.
Higuchi, Takuya, et al.. (2020). Stacked 1D Convolutional Networks for End-to-End Small Footprint Voice Trigger Detection. 2592–2596. 10 indexed citations
5.
Higuchi, Takuya, et al.. (2018). Measurement of a novel UHF RFID based battery-less vibration frequency sensing tag. International Symposium on Antennas and Propagation. 2 indexed citations
6.
Žmolíková, Kateřina, Marc Delcroix, Keisuke Kinoshita, et al.. (2018). Optimization of Speaker-Aware Multichannel Speech Extraction with ASR Criterion. 6 indexed citations
7.
Kameoka, Hirokazu, Takuya Higuchi, M. Tanaka, & Li Li. (2018). Nonnegative Matrix Factorization With Basis Clustering Using Cepstral Distance Regularization. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(6). 1029–1040. 5 indexed citations
8.
Žmolíková, Kateřina, Marc Delcroix, Keisuke Kinoshita, et al.. (2017). Speaker-Aware Neural Network Based Beamformer for Speaker Extraction in Speech Mixtures. 2655–2659. 67 indexed citations
9.
Higuchi, Takuya, Nobutaka Ito, Shoko Araki, et al.. (2017). Online MVDR Beamformer Based on Complex Gaussian Mixture Model With Spatial Prior for Noise Robust ASR. IEEE/ACM Transactions on Audio Speech and Language Processing. 25(4). 780–793. 76 indexed citations
10.
Delcroix, Marc, Keisuke Kinoshita, Atsunori Ogawa, et al.. (2017). Personalizing Your Speech Interface with Context Adaptive Deep Neural Networks. NTT technical review. 15(11). 35–39. 2 indexed citations
11.
Kinoshita, Keisuke, Marc Delcroix, Atsunori Ogawa, Takuya Higuchi, & Tomohiro Nakatani. (2017). Deep mixture density network for statistical model-based feature enhancement. 251–255. 7 indexed citations
12.
Nakatani, Tomohiro, Nobutaka Ito, Takuya Higuchi, Shoko Araki, & Keisuke Kinoshita. (2017). Integrating DNN-based and spatial clustering-based mask estimation for robust MVDR beamforming. 286–290. 46 indexed citations
13.
Higuchi, Takuya, Keisuke Kinoshita, Marc Delcroix, Kateřina Žmolíková, & Tomohiro Nakatani. (2017). Deep Clustering-Based Beamforming for Separation with Unknown Number of Sources. 1183–1187. 25 indexed citations
14.
Žmolíková, Kateřina, Marc Delcroix, Keisuke Kinoshita, et al.. (2017). Learning speaker representation for neural network based multichannel speaker extraction. 8–15. 33 indexed citations
15.
Higuchi, Takuya, Nobutaka Ito, Takuya Yoshioka, & Tomohiro Nakatani. (2016). Robust MVDR beamforming using time-frequency masks for online/offline ASR in noise. 5210–5214. 129 indexed citations
16.
Higuchi, Takuya, Norihiro Takamune, Tomohiko Nakamura, & Hirokazu Kameoka. (2014). Underdetermined blind separation and tracking of moving sources based ONDOA-HMM. 3191–3195. 8 indexed citations
17.
Higuchi, Takuya & Hirokazu Kameoka. (2014). Joint audio source separation and dereverberation based on multichannel factorial hidden Markov model. 1–6. 5 indexed citations
18.
Sakanashi, Hidenori, et al.. (2003). Evolvable hardware chips and their applications. 5. 559–564. 7 indexed citations
19.
Higuchi, Takuya, Masahiro Murakawa, Masaya Iwata, et al.. (2002). Evolvable hardware at function level. 187–192. 54 indexed citations
20.
Higuchi, Takuya, et al.. (1994). The IXM2 parallel associative processor for AI. Computer. 27(11). 53–63. 13 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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