Ambuj Mehrish
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Experimental and Cognitive Psychology
- Co-authors
- Navonil MajumderSoujanya PoriaRada MihalceaA V SubramanyamSabu EmmanuelDeepanway GhosalMohan KankanhalliRishabh Bhardwaj
- Topics
- Speech Recognition and Synthesis (8 papers)Digital Media Forensic Detection (7 papers)Speech and Audio Processing (6 papers)
In The Last Decade
Ambuj Mehrish
18 papers receiving 266 citations
Hit Papers
Peers
Comparison fields: 5 of 94
- Artificial Intelligence 103
- Signal Processing 99
- Computer Vision and Pattern Recognition 81
- Electrical and Electronic Engineering 19
- Experimental and Cognitive Psychology 14
Countries citing papers authored by Ambuj Mehrish
This map shows the geographic impact of Ambuj Mehrish'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 Ambuj Mehrish with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ambuj Mehrish more than expected).
Fields of papers citing papers by Ambuj Mehrish
This network shows the impact of papers produced by Ambuj Mehrish. 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 Ambuj Mehrish. The network helps show where Ambuj Mehrish may publish in the future.
Co-authorship network of co-authors of Ambuj Mehrish
This figure shows the co-authorship network connecting the top 25 collaborators of Ambuj Mehrish. A scholar is included among the top collaborators of Ambuj Mehrish 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 Ambuj Mehrish. Ambuj Mehrish 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 | 3 | |
| 3 | 2 | |
| 4 | A review of deep learning techniques for speech processingbreakdown → | 155 |
| 5 | 44 | |
| 6 | 6 | |
| 7 | 7 | |
| 8 | 3 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 5 | |
| 12 | 8 | |
| 13 | 7 | |
| 14 | 10 | |
| 15 | Anti-forensic technique for median filtering using L1-L2 TV model | 3 |
| 16 | 13 | |
| 17 | 6 | |
| 18 | 1 |
About Ambuj Mehrish
Ambuj Mehrish is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 18 papers that have together received 278 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (8 papers), Digital Media Forensic Detection (7 papers) and Speech and Audio Processing (6 papers). The work is most often cited by research in Signal Processing (99 citations), Computer Vision and Pattern Recognition (81 citations) and Artificial Intelligence (103 citations). Ambuj Mehrish has collaborated with scholars based in India, Singapore and Kuwait. Frequent co-authors include Navonil Majumder, Soujanya Poria, Rada Mihalcea, A V Subramanyam, Sabu Emmanuel, Deepanway Ghosal, Mohan Kankanhalli, Rishabh Bhardwaj, Shuai Zhao and Dorien Herremans. Their work appears in journals such as IEEE Access, IEEE Transactions on Circuits and Systems for Video Technology and Information Fusion.
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.