Slim Essid

2.5k citations
123 papers · 1.8k · h-index 24

Impact in

Papers in

Slim Essid

119 papers receiving 1.7k citations

Peers

Slim Essid
Comparison fields: 5 of 102
  • Signal Processing 1.3k
  • Computer Vision and Pattern Recognition 937
  • Developmental Biology 79
  • Music 84
  • Computational Mathematics 13
Replace Eric Battenberg with:
Eric Battenberg United States
Matt McVicar United Kingdom
Sourish Chaudhuri United States
Beth Logan United States
Oriol Nieto United States
Shigeki Sagayama Japan
Gautham J. Mysore United States
Olivier Lartillot Finland
Shawn Hershey United States
Zhiyao Duan United States
Slim Essid relative to Eric Battenberg United States Eric Battenberg's profile →
Citations per field
00.5×2×3×3.9×
Eric Battenberg · 1×
Citations per year

Countries citing papers authored by Slim Essid

Since Specialization
Citations

This map shows the geographic impact of Slim Essid'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 Slim Essid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Slim Essid more than expected).

Fields of papers citing papers by Slim Essid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Slim Essid. 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 Slim Essid. The network helps show where Slim Essid may publish in the future.

Co-authors

The 25 scholars most cited alongside Slim Essid, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Slim Essid Line = papers co-authored together Slim Essid links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 123 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010113
2 200598
3 200993
4 201782
5 200676
6 201767
7 201657
8 201146
9 201740
10 201540
11 201238
12 201438
13 200738
14 201731
15 201831
16 201230
17 201230
18 200429
19
SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION
201628
20 202327

About Slim Essid

Slim Essid is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience and Music, having authored 123 papers that have together received 1.8k indexed citations. Recurring topics across this work include Speech and Audio Processing (82 papers), Music and Audio Processing (79 papers), Music Technology and Sound Studies (35 papers), Speech Recognition and Synthesis (26 papers), Video Analysis and Summarization (14 papers), Blind Source Separation Techniques (12 papers), EEG and Brain-Computer Interfaces (8 papers) and Advanced Adaptive Filtering Techniques (7 papers). The work is most often cited by research in Signal Processing (1.3k citations), Computer Vision and Pattern Recognition (937 citations), Developmental Biology (79 citations), Music (84 citations) and Computational Mathematics (13 citations). Slim Essid has collaborated with scholars based in France, Belgium and United States. Frequent co-authors include Gaël Richard, Cyril Joder, Cédric Févotte, Bertrand David, Benoit Mathieu, Jacques Prado, Chloé Clavel, Olivier Cappé, Alexandre Gramfort and Olivier Gillet. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing, IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Transactions on Multimedia, Computer Speech & Language and Biomedical Signal Processing and Control.

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.

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