Andrew Gibiansky
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Experimental and Cognitive Psychology
- Co-authors
- Sercan Ö. ArıkAdam CoatesJoel HestnessRyan PrengerJ. J. MillerGregory DiamosJonathan RaimanRewon Child
- Topics
- Speech Recognition and Synthesis (5 papers)Speech and Audio Processing (4 papers)Music and Audio Processing (3 papers)
- Journals
- Neural Information Processing SystemsInternational Conference on Machine LearningICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Andrew Gibiansky
5 papers receiving 215 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 206
- Signal Processing 155
- Computer Vision and Pattern Recognition 31
- Electrical and Electronic Engineering 10
- Experimental and Cognitive Psychology 8
Countries citing papers authored by Andrew Gibiansky
This map shows the geographic impact of Andrew Gibiansky'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 Andrew Gibiansky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Gibiansky more than expected).
Fields of papers citing papers by Andrew Gibiansky
This network shows the impact of papers produced by Andrew Gibiansky. 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 Andrew Gibiansky. The network helps show where Andrew Gibiansky may publish in the future.
Co-authorship network of co-authors of Andrew Gibiansky
This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Gibiansky. A scholar is included among the top collaborators of Andrew Gibiansky 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 Andrew Gibiansky. Andrew Gibiansky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 2 | |
| 3 | Deep Voice 2: Multi-Speaker Neural Text-to-Speech. | 81 |
| 4 | Deep Voice: Real-time Neural Text-to-Speech | 39 |
| 5 | 106 |
About Andrew Gibiansky
Andrew Gibiansky is a scholar working on Signal Processing, Artificial Intelligence and Infectious Diseases, having authored 5 papers that have together received 235 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (5 papers), Speech and Audio Processing (4 papers) and Music and Audio Processing (3 papers). The work is most often cited by research in Signal Processing (155 citations), Artificial Intelligence (206 citations) and Computer Vision and Pattern Recognition (31 citations). Andrew Gibiansky has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Sercan Ö. Arık, Adam Coates, Joel Hestness, Ryan Prenger, J. J. Miller, Gregory Diamos, Jonathan Raiman, Rewon Child, Yanqi Zhou and Kainan Peng. Their work appears in journals such as Neural Information Processing Systems, International Conference on Machine Learning and ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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.