M. Krasner
Impact in
- Signal Processing top 10%
- Speech and Audio Processing
- Music and Audio Processing
-
- Speech Recognition and Synthesis
- Algorithms and Data Compression
- Speech and dialogue systems
- Natural Language Processing Techniques
- Bayesian Methods and Mixture Models
Papers in
-
- Speech Recognition and Synthesis 10
- Algorithms and Data Compression 2
- Speech and dialogue systems 2
- Natural Language Processing Techniques 1
-
- Speech and Audio Processing 10
- Music and Audio Processing 2
- Co-authors
- S. Roucos (7 shared papers)Richard Schwartz (8 shared papers)J. Makhoul (5 shared papers)Y.-L. Chow (3 shared papers)Owen Kimball (4 shared papers)Jared J. Wolf (4 shared papers)H. Gish (3 shared papers)P. Price (2 shared papers)
- Journals
- The Journal of the Acoustical Society of America (2 papers)Proceedings of the International Conference on Parallel Processing (1 paper)
- Partner nations
- United States
In The Last Decade
M. Krasner
10 papers receiving 70 citations
Peers
Comparison fields: 5 of 27
- Signal Processing 66
- Artificial Intelligence 85
- Computer Vision and Pattern Recognition 36
- Hardware and Architecture 3
- Sensory Systems 2
Countries citing papers authored by M. Krasner
This map shows the geographic impact of M. Krasner'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 M. Krasner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Krasner more than expected).
Fields of papers citing papers by M. Krasner
This network shows the impact of papers produced by M. Krasner. 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 M. Krasner. The network helps show where M. Krasner may publish in the future.
Co-authors
The 15 scholars most cited alongside M. Krasner, 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 | 2005 | 28 | |
| 2 | 2005 | 24 | |
| 3 | 2005 | 15 | |
| 4 | 2005 | 9 | |
| 5 | 2005 | 7 | |
| 6 | 2005 | 7 | |
| 7 | 1982 | 5 | |
| 8 | 2005 | 4 | |
| 9 | 2005 | 3 | |
| 10 | 2005 | 2 | |
| 11 | Continuous Speech Recognition on a Butterfly Parallel Processor. | 1986 | 1 |
| 12 | 2005 | 1 | |
| 13 | 1979 | 1 | |
| 14 | 2005 | 0 | |
| 15 | 2005 | 0 |
About M. Krasner
M. Krasner is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Computational Mechanics and Civil and Structural Engineering, having authored 15 papers that have together received 107 indexed citations. Recurring topics across this work include Speech and Audio Processing (10 papers), Speech Recognition and Synthesis (10 papers), Advanced Data Compression Techniques (7 papers), Advanced Adaptive Filtering Techniques (2 papers), Music and Audio Processing (2 papers), Algorithms and Data Compression (2 papers), Speech and dialogue systems (2 papers) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Signal Processing (66 citations), Artificial Intelligence (85 citations), Computer Vision and Pattern Recognition (36 citations), Hardware and Architecture (3 citations) and Sensory Systems (2 citations). M. Krasner has collaborated with scholars based in United States. Frequent co-authors include S. Roucos, Richard Schwartz, J. Makhoul, Y.-L. Chow, Owen Kimball, Jared J. Wolf, H. Gish, P. Price, M. Dunham and Francis Kubala. Their work appears in journals such as The Journal of the Acoustical Society of America and Proceedings of the International Conference on Parallel Processing.
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.