Kuldip K. Paliwal
- Signal Processing top 0.05%
- Speech and Audio Processing 160
- Music and Audio Processing 42
- Blind Source Separation Techniques 38
- Artificial Intelligence top 0.1%
- Speech Recognition and Synthesis 95
- Computational Mechanics top 0.5%
- Advanced Adaptive Filtering Techniques 63
- Molecular Biology top 2%
- Machine Learning in Bioinformatics 38
- Protein Structure and Dynamics 35
- RNA and protein synthesis mechanisms 29
Kuldip K. Paliwal
237 papers receiving 12.9k citations
Hit Papers
Peers
Comparison fields: 5 of 210
- Signal Processing 3.9k
- Artificial Intelligence 5.0k
- Computer Vision and Pattern Recognition 2.1k
- Computational Mechanics 1.2k
- Molecular Biology 3.5k
Countries citing papers authored by Kuldip K. Paliwal
This map shows the geographic impact of Kuldip K. Paliwal'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 Kuldip K. Paliwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kuldip K. Paliwal more than expected).
Fields of papers citing papers by Kuldip K. Paliwal
This network shows the impact of papers produced by Kuldip K. Paliwal. 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 Kuldip K. Paliwal. The network helps show where Kuldip K. Paliwal may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kuldip K. Paliwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 2 | |
| 2 | 2024 | 10 | |
| 3 | 2022 | 34 | |
| 4 | 2022 | 2 | |
| 5 | 2021 | 26 | |
| 6 | 2021 | 13 | |
| 7 | 2021 | 2 | |
| 8 | 2021 | 10 | |
| 9 | 2021 | 3 | |
| 10 | 2021 | 15 | |
| 11 | 2020 | 104 | |
| 12 | Deep Residual Network-Based Augmented Kalman Filter for Speech Enhancement | 2020 | 3 |
| 13 | 2019 | 9 | |
| 14 | 2019 | 15 | |
| 15 | 2013 | 43 | |
| 16 | Diphone-Based Concatenative Speech Synthesis System for Mongolian | 2008 | 3 |
| 17 | Representing frequencies in speech. | 2003 | 3 |
| 18 | Usefulness of phase in human speech perception. | 2003 | 1 |
| 19 | Automatic Speech and Speaker Recognition: Advanced Topics | 1999 | 114 |
| 20 | 1983 | 7 |
About Kuldip K. Paliwal
Kuldip K. Paliwal is a scholar working on Signal Processing, Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 250 papers that have together received 13.6k indexed citations. Recurring topics across this work include Speech and Audio Processing (160 papers), Speech Recognition and Synthesis (95 papers), Advanced Adaptive Filtering Techniques (63 papers), Music and Audio Processing (42 papers), Machine Learning in Bioinformatics (38 papers), Blind Source Separation Techniques (38 papers), Protein Structure and Dynamics (35 papers) and RNA and protein synthesis mechanisms (29 papers). The work is most often cited by research in Signal Processing (3.9k citations), Artificial Intelligence (5.0k citations), Computer Vision and Pattern Recognition (2.1k citations), Computational Mechanics (1.2k citations) and Molecular Biology (3.5k citations). Kuldip K. Paliwal has collaborated with scholars based in Australia, India and Fiji. Frequent co-authors include Mike Schuster, Yaoqi Zhou, Alok Sharma, Yuedong Yang, James Lyons, Jack Hanson, Abdollah Dehzangi, Kamil Wójcicki, Rhys Heffernan and Leigh D. Alsteris. Their work appears in journals such as Speech Communication, Bioinformatics, The Journal of the Acoustical Society of America, Signal Processing and Journal of Theoretical Biology.
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.