Chin‐Tuan Tan

827 total citations
37 papers, 631 citations indexed

About

Chin‐Tuan Tan is a scholar working on Cognitive Neuroscience, Signal Processing and Speech and Hearing. According to data from OpenAlex, Chin‐Tuan Tan has authored 37 papers receiving a total of 631 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Cognitive Neuroscience, 23 papers in Signal Processing and 19 papers in Speech and Hearing. Recurrent topics in Chin‐Tuan Tan's work include Hearing Loss and Rehabilitation (31 papers), Speech and Audio Processing (22 papers) and Noise Effects and Management (19 papers). Chin‐Tuan Tan is often cited by papers focused on Hearing Loss and Rehabilitation (31 papers), Speech and Audio Processing (22 papers) and Noise Effects and Management (19 papers). Chin‐Tuan Tan collaborates with scholars based in United States, China and United Kingdom. Chin‐Tuan Tan's co-authors include Brian C. J. Moore, Y. C. Tong, Nick Zacharov, Mario A. Svirsky, Brett Martin, Matthew B. Fitzgerald, Elad Sagi, Daniel Jethanamest, J. Thomas Roland and Bernie Caessens and has published in prestigious journals such as The Journal of the Acoustical Society of America, Journal of Neurology Neurosurgery & Psychiatry and The Laryngoscope.

In The Last Decade

Chin‐Tuan Tan

33 papers receiving 589 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chin‐Tuan Tan United States 12 425 387 205 138 125 37 631
Dirk Püschel Germany 7 497 1.2× 361 0.9× 198 1.0× 74 0.5× 128 1.0× 15 623
Paul Calamia United States 14 286 0.7× 289 0.7× 112 0.5× 78 0.6× 60 0.5× 54 531
David M. Thompson United States 6 507 1.2× 439 1.1× 125 0.6× 110 0.8× 55 0.4× 10 743
Tim Van den Bogaert Belgium 11 605 1.4× 519 1.3× 241 1.2× 286 2.1× 123 1.0× 34 748
Waldo Nogueira Germany 18 769 1.8× 487 1.3× 286 1.4× 54 0.4× 285 2.3× 81 879
Sarah E. Yoho United States 11 432 1.0× 488 1.3× 122 0.6× 162 1.2× 43 0.3× 32 640
Hendrik Kayser Germany 8 306 0.7× 336 0.9× 95 0.5× 140 1.0× 30 0.2× 32 441
Tobias Goehring United Kingdom 16 506 1.2× 300 0.8× 166 0.8× 78 0.6× 147 1.2× 30 593
William S. Woods United States 8 405 1.0× 363 0.9× 200 1.0× 85 0.6× 67 0.5× 14 584
Tobias May Denmark 15 379 0.9× 656 1.7× 88 0.4× 192 1.4× 33 0.3× 71 765

Countries citing papers authored by Chin‐Tuan Tan

Since Specialization
Citations

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

Fields of papers citing papers by Chin‐Tuan Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chin‐Tuan Tan. 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 Chin‐Tuan Tan. The network helps show where Chin‐Tuan Tan may publish in the future.

Co-authorship network of co-authors of Chin‐Tuan Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Chin‐Tuan Tan. A scholar is included among the top collaborators of Chin‐Tuan Tan 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 Chin‐Tuan Tan. Chin‐Tuan Tan 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.
Lee, Sungmin, et al.. (2022). Cross-Frequency Coupling in Cortical Processing of Speech. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 25–29.
2.
Lee, Sungmin, et al.. (2022). Cortical Entrainment to Speech Produced by Cochlear Implant Users and Normal-Hearing Talkers. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022. 3577–3581.
3.
Lee, Sungmin, et al.. (2022). Cortical entrainment to speech produced by cochlear implant talkers and normal-hearing talkers. Frontiers in Neuroscience. 16. 927872–927872.
4.
Lee, Sungmin, et al.. (2020). Neural Tracking of Band-Limited Sine-Wave Speech in Normal Hearing and Cochlear Implant Listeners. 8(1). 38–49. 3 indexed citations
5.
6.
Heydarzadeh, Mehrdad, Chin‐Tuan Tan, Mehrdad Nourani, & Sarah Ostadabbas. (2017). Gait variability assessment in neuro-degenerative patients by measuring complexity of independent sources. PubMed. 2017. 3186–3189. 2 indexed citations
7.
Fitzgerald, Matthew B., et al.. (2016). Self-Selection of Frequency Tables with Bilateral Mismatches in an Acoustic Simulation of a Cochlear Implant. Journal of the American Academy of Audiology. 28(5). 385–394. 7 indexed citations
8.
Tan, Chin‐Tuan, Brett Martin, & Mario A. Svirsky. (2016). Pitch Matching between Electrical Stimulation of a Cochlear Implant and Acoustic Stimuli Presented to a Contralateral Ear with Residual Hearing. Journal of the American Academy of Audiology. 28(3). 187–199. 33 indexed citations
9.
10.
Fitzgerald, Matthew B., et al.. (2013). Feasibility of Real-Time Selection of Frequency Tables in an Acoustic Simulation of a Cochlear Implant. Ear and Hearing. 34(6). 763–772. 16 indexed citations
11.
Svirsky, Mario A., Nai Ding, Elad Sagi, et al.. (2013). Validation of acoustic models of auditory neural prostheses. PubMed. 2013. 8629–8633. 11 indexed citations
12.
Tan, Chin‐Tuan, Mario A. Svirsky, Bernie Caessens, et al.. (2013). Real‐time measurement of electrode impedance during intracochlear electrode insertion. The Laryngoscope. 123(4). 1028–1032. 33 indexed citations
13.
Svirsky, Mario A., Matthew B. Fitzgerald, Arlene C. Neuman, et al.. (2012). Current and Planned Cochlear Implant Research at New York University Laboratory for Translational Auditory Research. Journal of the American Academy of Audiology. 23(6). 422–437. 6 indexed citations
14.
Tan, Chin‐Tuan, et al.. (2012). Behavioral and physiological measure for pitch matching between electrical and acoustical stimulation in cochlear implant patients. The Journal of the Acoustical Society of America. 131(4_Supplement). 3388–3388. 4 indexed citations
15.
Jethanamest, Daniel, Chin‐Tuan Tan, Matthew B. Fitzgerald, & Mario A. Svirsky. (2010). A New Software Tool to Optimize Frequency Table Selection for Cochlear Implants. Otology & Neurotology. 31(8). 1242–1247. 16 indexed citations
16.
Tan, Chin‐Tuan & Brian C. J. Moore. (2008). Perception of nonlinear distortion by hearing-impaired people. International Journal of Audiology. 47(5). 246–256. 34 indexed citations
17.
Moore, Brian C. J. & Chin‐Tuan Tan. (2004). Development and Validation of a Method for Predicting the Perceived Naturalness of Sounds Subjected to Spectral Distortion. Journal of the Audio Engineering Society. 52(9). 900–914. 27 indexed citations
18.
Tan, Chin‐Tuan, et al.. (2004). Predicting the Perceived Quality of Nonlinearly Distorted Music and Speech Signals. Journal of the Audio Engineering Society. 52(7). 699–711. 35 indexed citations
19.
Moore, Brian C. J., et al.. (2004). Measuring and Predicting the Perceived Quality of Music and Speech Subjected to Combined Linear and Nonlinear Distortion. Journal of the Audio Engineering Society. 52(12). 1228–1244. 23 indexed citations
20.
Tan, Chin‐Tuan, Brian C. J. Moore, & Nick Zacharov. (2003). The Effect of Nonlinear Distortion on the Perceived Quality of Music and Speech Signals. Journal of the Audio Engineering Society. 51(11). 1012–1031. 48 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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