Brandon G. Morton
- Signal Processing top 2%
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience top 10%
- Experimental and Cognitive Psychology
- Artificial Intelligence
- Co-authors
- Youngmoo E. KimErik M. SchmidtRaymond MignecoPatrick RichardsonJeffrey J. ScottDouglas TurnbullCoray M. ColinaIbrahim M. Moustafa
- Topics
- Music and Audio Processing (8 papers)Music Technology and Sound Studies (8 papers)Speech and Audio Processing (5 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of Molecular BiologyZenodo (CERN European Organization for Nuclear Research)
- Partner nations
- United StatesSouth Sudan
In The Last Decade
Brandon G. Morton
14 papers receiving 416 citations
Peers
Comparison fields: 5 of 66
- Signal Processing 293
- Computer Vision and Pattern Recognition 179
- Cognitive Neuroscience 143
- Experimental and Cognitive Psychology 63
- Artificial Intelligence 59
Countries citing papers authored by Brandon G. Morton
This map shows the geographic impact of Brandon G. Morton'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 Brandon G. Morton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brandon G. Morton more than expected).
Fields of papers citing papers by Brandon G. Morton
This network shows the impact of papers produced by Brandon G. Morton. 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 Brandon G. Morton. The network helps show where Brandon G. Morton may publish in the future.
Co-authorship network of co-authors of Brandon G. Morton
This figure shows the co-authorship network connecting the top 25 collaborators of Brandon G. Morton. A scholar is included among the top collaborators of Brandon G. Morton 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 Brandon G. Morton. Brandon G. Morton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 19 | |
| 4 | 6 | |
| 5 | 2 | |
| 6 | 6 | |
| 7 | Predicting Time-Varying Musical Emotion Distributions from Multi-Track Audio | 2 |
| 8 | Relating Perceptual and Feature Space Invariances in Music Emotion Recognition | 3 |
| 9 | 70 | |
| 10 | 12 | |
| 11 | 41 | |
| 12 | 3 | |
| 13 | Music emotion recognition: A state of the art review | 239 |
| 14 | 1 | |
| 15 | 15 | |
| 16 | 33 |
About Brandon G. Morton
Brandon G. Morton is a scholar working on Architecture, Signal Processing and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 455 indexed citations. Recurring topics across this work include Music and Audio Processing (8 papers), Music Technology and Sound Studies (8 papers) and Speech and Audio Processing (5 papers). The work is most often cited by research in Signal Processing (293 citations), Computer Vision and Pattern Recognition (179 citations) and Cognitive Neuroscience (143 citations). Brandon G. Morton has collaborated with scholars based in United States and South Sudan. Frequent co-authors include Youngmoo E. Kim, Erik M. Schmidt, Raymond Migneco, Patrick Richardson, Jeffrey J. Scott, Douglas Turnbull, Coray M. Colina, Ibrahim M. Moustafa, Craig E. Cameron and Hujun Shen. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Molecular Biology 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.