Matt Sharifi
- Artificial Intelligence top 10%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 10%
- Cognitive Neuroscience
- Experimental and Cognitive Psychology
- Co-authors
- Marco TagliasacchiNeil ZeghidourOlivier PietquinRaphaël MarinierEugene KharitonovDamien VincentZalán BorsosDominik Roblek
- Topics
- Speech and Audio Processing (3 papers)Speech Recognition and Synthesis (3 papers)Music and Audio Processing (3 papers)
- Journals
- IEEE/ACM Transactions on Audio Speech and Language ProcessingTransactions of the Association for Computational LinguisticsZenodo (CERN European Organization for Nuclear Research)
- Partner nations
- United StatesSwitzerland
In The Last Decade
Matt Sharifi
4 papers receiving 282 citations
Hit Papers
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 179
- Signal Processing 154
- Computer Vision and Pattern Recognition 83
- Cognitive Neuroscience 13
- Experimental and Cognitive Psychology 12
Countries citing papers authored by Matt Sharifi
This map shows the geographic impact of Matt Sharifi'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 Sharifi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matt Sharifi more than expected).
Fields of papers citing papers by Matt Sharifi
This network shows the impact of papers produced by Matt Sharifi. 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 Sharifi. The network helps show where Matt Sharifi may publish in the future.
Co-authorship network of co-authors of Matt Sharifi
This figure shows the co-authorship network connecting the top 25 collaborators of Matt Sharifi. A scholar is included among the top collaborators of Matt Sharifi 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 Sharifi. Matt Sharifi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | AudioLM: A Language Modeling Approach to Audio Generationbreakdown → | 201 |
| 2 | 54 | |
| 3 | 4 | |
| 4 | 39 |
About Matt Sharifi
Matt Sharifi is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 4 papers that have together received 298 indexed citations. Recurring topics across this work include Speech and Audio Processing (3 papers), Speech Recognition and Synthesis (3 papers) and Music and Audio Processing (3 papers). The work is most often cited by research in Signal Processing (154 citations), Artificial Intelligence (179 citations) and Computer Vision and Pattern Recognition (83 citations). Matt Sharifi has collaborated with scholars based in United States and Switzerland. Frequent co-authors include Marco Tagliasacchi, Neil Zeghidour, Olivier Pietquin, Raphaël Marinier, Eugene Kharitonov, Damien Vincent, Zalán Borsos, Dominik Roblek, Olivier Teboul and David Grangier. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Transactions of the Association for Computational Linguistics 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.