Chris Cannam
Impact in
- Signal Processing top 2%
- Music and Audio Processing
- Speech and Audio Processing
- Music top 2%
- Diverse Musicological Studies
- Diverse Music Education Insights
Papers in
-
- Music and Audio Processing 8
- Speech and Audio Processing 3
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- Music Technology and Sound Studies 7
- Co-authors
- M. Sandler (4 shared papers)Juan Pablo Bello (4 shared papers)Matthias Mauch (3 shared papers)Rachel Bittner (3 shared papers)Justin Salamon (3 shared papers)Simon Dixon (1 shared paper)Jia Le Dai (1 shared paper)Mark d’Inverno (1 shared paper)
- Journals
- Journal of New Music Research (1 paper)Queen Mary Research Online (Queen Mary University of London) (2 papers)International Symposium/Conference on Music Information Retrieval (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)View (1 paper)
- Partner nations
- United KingdomSwitzerlandChina
In The Last Decade
Chris Cannam
8 papers receiving 440 citations
Peers
Comparison fields: 5 of 65
- Signal Processing 391
- Music 67
- Computer Vision and Pattern Recognition 298
- Developmental Biology 20
- Cognitive Neuroscience 143
Countries citing papers authored by Chris Cannam
This map shows the geographic impact of Chris Cannam'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 Chris Cannam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Cannam more than expected).
Fields of papers citing papers by Chris Cannam
This network shows the impact of papers produced by Chris Cannam. 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 Chris Cannam. The network helps show where Chris Cannam may publish in the future.
Co-authors
The 10 scholars most cited alongside Chris Cannam, 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 | 2010 | 162 | |
| 2 | 2014 | 147 | |
| 3 | 2006 | 76 | |
| 4 | 2015 | 65 | |
| 5 | 2010 | 35 | |
| 6 | 2012 | 3 | |
| 7 | Piper: Audio Feature Extraction in Browser and Mobile Applications | 2017 | 3 |
| 8 | MedleyDB: A multitrack dataset for annotation-intensive MIR research. 15th International Society for Music Information Retrieval Conference, ISMIR 2014 | 2014 | 1 |
About Chris Cannam
Chris Cannam is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Music, Cognitive Neuroscience and Artificial Intelligence, having authored 8 papers that have together received 492 indexed citations. Recurring topics across this work include Music and Audio Processing (8 papers), Music Technology and Sound Studies (7 papers), Speech and Audio Processing (3 papers), Diverse Musicological Studies (2 papers), Neuroscience and Music Perception (2 papers) and Speech Recognition and Synthesis (1 paper). The work is most often cited by research in Signal Processing (391 citations), Music (67 citations), Computer Vision and Pattern Recognition (298 citations), Developmental Biology (20 citations) and Cognitive Neuroscience (143 citations). Chris Cannam has collaborated with scholars based in United Kingdom, Switzerland and China. Frequent co-authors include M. Sandler, Juan Pablo Bello, Matthias Mauch, Rachel Bittner, Justin Salamon, Simon Dixon, Jia Le Dai, Mark d’Inverno, Christophe Rhodes and Mark D. Plumbley. Their work appears in journals such as Journal of New Music Research, Queen Mary Research Online (Queen Mary University of London), International Symposium/Conference on Music Information Retrieval, Zenodo (CERN European Organization for Nuclear Research) and View.
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.