Matt McVicar
- Signal Processing top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 2%
- Experimental and Cognitive Psychology top 5%
- Cognitive Neuroscience top 5%
- Co-authors
- Daniel P. W. EllisColin RaffelDawen LiangBrian McFeeOriol NietoEric BattenbergTijl De BieRaúl Santos‐Rodríguez
- Topics
- Music and Audio Processing (17 papers)Music Technology and Sound Studies (12 papers)Speech and Audio Processing (9 papers)
- Journals
- Pattern Recognition LettersIEEE Transactions on Audio Speech and Language ProcessingIEEE/ACM Transactions on Audio Speech and Language Processing
- Partner nations
- United KingdomUnited StatesJapan
In The Last Decade
Matt McVicar
22 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 116
- Signal Processing 1.4k
- Computer Vision and Pattern Recognition 647
- Artificial Intelligence 548
- Experimental and Cognitive Psychology 300
- Cognitive Neuroscience 271
Countries citing papers authored by Matt McVicar
This map shows the geographic impact of Matt McVicar'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 Matt McVicar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matt McVicar more than expected).
Fields of papers citing papers by Matt McVicar
This network shows the impact of papers produced by Matt McVicar. 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 Matt McVicar. The network helps show where Matt McVicar may publish in the future.
Co-authorship network of co-authors of Matt McVicar
This figure shows the co-authorship network connecting the top 25 collaborators of Matt McVicar. A scholar is included among the top collaborators of Matt McVicar 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 Matt McVicar. Matt McVicar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | 3 | |
| 3 | 6 | |
| 4 | librosa: Audio and Music Signal Analysis in Pythonbreakdown → | 1676 |
| 5 | 7 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 14 | |
| 9 | Trend Extraction on Twitter Time Series for Music Discovery | 2 |
| 10 | 8 | |
| 11 | 2 | |
| 12 | 40 | |
| 13 | 23 | |
| 14 | CCA and a Multi-way Extension for Investigating Common Components between Audio, Lyrics and Tags. | 2 |
| 15 | 35 | |
| 16 | 4 | |
| 17 | 22 | |
| 18 | 5 | |
| 19 | 5 | |
| 20 | 3 |
About Matt McVicar
Matt McVicar is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Developmental Biology, having authored 23 papers that have together received 1.9k indexed citations. Recurring topics across this work include Music and Audio Processing (17 papers), Music Technology and Sound Studies (12 papers) and Speech and Audio Processing (9 papers). The work is most often cited by research in Signal Processing (1.4k citations), Developmental Biology (138 citations) and Computer Vision and Pattern Recognition (647 citations). Matt McVicar has collaborated with scholars based in United Kingdom, United States and Japan. Frequent co-authors include Daniel P. W. Ellis, Colin Raffel, Dawen Liang, Brian McFee, Oriol Nieto, Eric Battenberg, Tijl De Bie, Raúl Santos‐Rodríguez, Yizhao Ni and Masataka Goto. Their work appears in journals such as Pattern Recognition Letters, IEEE Transactions on Audio Speech and Language Processing and IEEE/ACM Transactions on Audio Speech and Language 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.