Arun Narayanan

4.0k citations
69 papers · 2.4k indexed · 2 hit papers · h-index 19

Arun Narayanan

60 papers receiving 2.2k citations

Hit Papers

On Training Targets for Supervised Speech Separation7842013202620172021250500750

Peers

Arun Narayanan
Comparison fields: 5 of 82
  • Signal Processing 2.1k
  • Artificial Intelligence 1.6k
  • Computational Mechanics 640
  • Cognitive Neuroscience 361
  • Pharmacy 38
Replace Marc Delcroix with:
Marc Delcroix Japan
Yi Luo China
Jitong Chen United States
Zhong-Qiu Wang United States
Ashutosh Pandey United States
Ke Tan United States
Andrew Varga United Kingdom
Chengshi Zheng China
Cees Taal Netherlands
Hans‐Günter Hirsch Germany
Arun Narayanan relative to Marc Delcroix Japan Marc Delcroix's profile →
Citations per field
00.5×1.5×
Marc Delcroix · 1×
Citations per year

Countries citing papers authored by Arun Narayanan

Since Specialization
Citations

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

Fields of papers citing papers by Arun Narayanan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Arun Narayanan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Arun Narayanan Line = papers co-authored together Arun Narayanan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20241
4 20242
5 20244
6 20241
7 20240
8 20231
9 202219
10 20220
11 20222
12 202110
13 202167
14 20209
15 20200
16
The SYNTAX II Score Predicts Mortality at 4 Years in Patients Undergoing Percutaneous Coronary Intervention.
20186
17 20174
18 201532
19 201445
20
On Training Targets for Supervised Speech Separationbreakdown →
2014784

About Arun Narayanan

Arun Narayanan is a scholar working on Signal Processing, Artificial Intelligence and Computational Mechanics, having authored 69 papers that have together received 2.4k indexed citations. Recurring topics across this work include Speech and Audio Processing (42 papers), Speech Recognition and Synthesis (39 papers), Music and Audio Processing (27 papers), Advanced Adaptive Filtering Techniques (10 papers), Cardiac pacing and defibrillation studies (5 papers), Hearing Loss and Rehabilitation (4 papers), Topic Modeling (3 papers) and Atrial Fibrillation Management and Outcomes (3 papers). The work is most often cited by research in Signal Processing (2.1k citations), Artificial Intelligence (1.6k citations) and Computational Mechanics (640 citations). Arun Narayanan has collaborated with scholars based in United States, India and Germany. Frequent co-authors include DeLiang Wang, Yuxuan Wang, Tara N. Sainath, Michiel Bacchiani, Ananya Misra, K. K. Chin, Kevin Wilson, Ron J. Weiss, Izhak Shafran and Ehsan Variani. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, SAE technical papers on CD-ROM/SAE technical paper series, IEEE Transactions on Audio Speech and Language Processing, The Journal of the Acoustical Society of America and Journal of the American College of Cardiology.

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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