Arvindh Krishnaswamy
- Signal Processing top 5%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Cognitive Neuroscience
- Computational Mechanics
- Co-authors
- Ritwik GiriJean-Marc ValinFangzhou ChengSrikanth V. TennetiParis SmaragdisChristopher MontgomeryTurab IqbalWenwu Wang
- Topics
- Speech and Audio Processing (9 papers)Music and Audio Processing (9 papers)Speech Recognition and Synthesis (6 papers)
- Journals
- Journal of the Audio Engineering SocietyICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Interspeech 2022
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Arvindh Krishnaswamy
13 papers receiving 171 citations
Peers
Comparison fields: 5 of 36
- Signal Processing 163
- Artificial Intelligence 103
- Computer Vision and Pattern Recognition 45
- Cognitive Neuroscience 27
- Computational Mechanics 25
Countries citing papers authored by Arvindh Krishnaswamy
This map shows the geographic impact of Arvindh Krishnaswamy'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 Arvindh Krishnaswamy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arvindh Krishnaswamy more than expected).
Fields of papers citing papers by Arvindh Krishnaswamy
This network shows the impact of papers produced by Arvindh Krishnaswamy. 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 Arvindh Krishnaswamy. The network helps show where Arvindh Krishnaswamy may publish in the future.
Co-authorship network of co-authors of Arvindh Krishnaswamy
This figure shows the co-authorship network connecting the top 25 collaborators of Arvindh Krishnaswamy. A scholar is included among the top collaborators of Arvindh Krishnaswamy 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 Arvindh Krishnaswamy. Arvindh Krishnaswamy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 7 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 15 | |
| 6 | 14 | |
| 7 | 5 | |
| 8 | Group masked autoencoder based density estimator for audio anomaly detection | 7 |
| 9 | Self-supervised classification for detecting anomalous sounds | 21 |
| 10 | 70 | |
| 11 | 3 | |
| 12 | 22 | |
| 13 | On the Twelve Basic Intervals in South Indian Classical Music | 8 |
About Arvindh Krishnaswamy
Arvindh Krishnaswamy is a scholar working on Signal Processing, Music and Artificial Intelligence, having authored 13 papers that have together received 184 indexed citations. Recurring topics across this work include Speech and Audio Processing (9 papers), Music and Audio Processing (9 papers) and Speech Recognition and Synthesis (6 papers). The work is most often cited by research in Signal Processing (163 citations), Artificial Intelligence (103 citations) and Computer Vision and Pattern Recognition (45 citations). Arvindh Krishnaswamy has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Ritwik Giri, Jean-Marc Valin, Fangzhou Cheng, Srikanth V. Tenneti, Paris Smaragdis, Christopher Montgomery, Turab Iqbal, Wenwu Wang, Erfan Soltanmohammadi and Michael M. Goodwin. Their work appears in journals such as Journal of the Audio Engineering Society, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) and Interspeech 2022.
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.