G. Kang
- Signal Processing top 5%
- Computational Mechanics top 5%
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Electrical and Electronic Engineering
- Topics
- Speech and Audio Processing (17 papers)Advanced Adaptive Filtering Techniques (10 papers)Speech Recognition and Synthesis (8 papers)
- Journals
- IEEE Transactions on Circuits and SystemsIEEE Transactions on Acoustics Speech and Signal ProcessingZenodo (CERN European Organization for Nuclear Research)
- Partner nations
- United States
In The Last Decade
G. Kang
21 papers receiving 232 citations
Peers
Comparison fields: 5 of 37
- Signal Processing 205
- Computational Mechanics 149
- Computer Vision and Pattern Recognition 85
- Artificial Intelligence 57
- Electrical and Electronic Engineering 16
Countries citing papers authored by G. Kang
This map shows the geographic impact of G. Kang'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 G. Kang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Kang more than expected).
Fields of papers citing papers by G. Kang
This network shows the impact of papers produced by G. Kang. 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 G. Kang. The network helps show where G. Kang may publish in the future.
Co-authorship network of co-authors of G. Kang
This figure shows the co-authorship network connecting the top 25 collaborators of G. Kang. A scholar is included among the top collaborators of G. Kang 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 G. Kang. G. Kang 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 | Hiding Information under Speech | 7 |
| 3 | 0 | |
| 4 | 10 | |
| 5 | 0 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 2 | |
| 9 | 2 | |
| 10 | 7 | |
| 11 | Speech Analysis and Synthesis Based on Pitch-Synchronous Segmentation of the Speech Waveform. | 6 |
| 12 | 28 | |
| 13 | 14 | |
| 14 | 4 | |
| 15 | 96 | |
| 16 | 17 | |
| 17 | 21 | |
| 18 | Second Report of the Multirate Processor (MRP) for Digital Voice Communications. | 4 |
| 19 | Improvement of the narrowband linear predictive coder. Part 2: Synthesis improvements | 6 |
| 20 | 7 |
About G. Kang
G. Kang is a scholar working on Signal Processing, Computational Mechanics and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 251 indexed citations. Recurring topics across this work include Speech and Audio Processing (17 papers), Advanced Adaptive Filtering Techniques (10 papers) and Speech Recognition and Synthesis (8 papers). The work is most often cited by research in Signal Processing (205 citations), Computational Mechanics (149 citations) and Computer Vision and Pattern Recognition (85 citations). G. Kang has collaborated with scholars based in United States. Frequent co-authors include Wasfy B. Mikhael, Feng Wu, L.G. Kazovsky, Thomas M. Moran and Andreas Spanias. Their work appears in journals such as IEEE Transactions on Circuits and Systems, IEEE Transactions on Acoustics Speech and Signal Processing and Zenodo (CERN European Organization for Nuclear Research).
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.