Homayoon Beigi
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 10%
- Civil and Structural Engineering top 10%
- Control and Systems Engineering top 10%
- Co-authors
- Raimondo BettiJ. SubrahmoniaK. S. NathanGregory J. ClaryHiroshi MaruyamaMaria Q. FengShengyi LiMahesh Viswanathan
- Topics
- Music and Audio Processing (8 papers)Speech Recognition and Synthesis (8 papers)Speech and Audio Processing (4 papers)
- Journals
- Journal of Sound and VibrationMechanical Systems and Signal ProcessingJournal of Dynamic Systems Measurement and Control
- Partner nations
- United StatesAustralia
In The Last Decade
Homayoon Beigi
25 papers receiving 409 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 220
- Signal Processing 187
- Computer Vision and Pattern Recognition 113
- Civil and Structural Engineering 86
- Control and Systems Engineering 71
Countries citing papers authored by Homayoon Beigi
This map shows the geographic impact of Homayoon Beigi'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 Homayoon Beigi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Homayoon Beigi more than expected).
Fields of papers citing papers by Homayoon Beigi
This network shows the impact of papers produced by Homayoon Beigi. 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 Homayoon Beigi. The network helps show where Homayoon Beigi may publish in the future.
Co-authorship network of co-authors of Homayoon Beigi
This figure shows the co-authorship network connecting the top 25 collaborators of Homayoon Beigi. A scholar is included among the top collaborators of Homayoon Beigi 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 Homayoon Beigi. Homayoon Beigi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 13 | |
| 4 | 54 | |
| 5 | 173 | |
| 6 | 0 | |
| 7 | Voice: technologies and algorithms for biometrics applications | 2 |
| 8 | 1 | |
| 9 | 4 | |
| 10 | 10 | |
| 11 | 1 | |
| 12 | 10 | |
| 13 | 48 | |
| 14 | 13 | |
| 15 | 1 | |
| 16 | An Overview of Handwriting Recognition | 8 |
| 17 | 19 | |
| 18 | Learning Algorithms for Neural Networks Based on Quasi-Newton Method with Self-Scaling | 2 |
| 19 | 30 | |
| 20 | 1 |
About Homayoon Beigi
Homayoon Beigi is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 27 papers that have together received 446 indexed citations. Recurring topics across this work include Music and Audio Processing (8 papers), Speech Recognition and Synthesis (8 papers) and Speech and Audio Processing (4 papers). The work is most often cited by research in Signal Processing (187 citations), Artificial Intelligence (220 citations) and Computer Vision and Pattern Recognition (113 citations). Homayoon Beigi has collaborated with scholars based in United States and Australia. Frequent co-authors include Raimondo Betti, J. Subrahmonia, K. S. Nathan, Gregory J. Clary, Hiroshi Maruyama, Maria Q. Feng, Shengyi Li, Mahesh Viswanathan, Jeffrey Sorensen and Stéphane Maes. Their work appears in journals such as Journal of Sound and Vibration, Mechanical Systems and Signal Processing and Journal of Dynamic Systems Measurement and Control.
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.