Leonardo Gabrielli

96 total papers · 727 total citations
57 papers, 470 citations indexed

About

Leonardo Gabrielli is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Leonardo Gabrielli has authored 57 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Signal Processing, 25 papers in Computer Vision and Pattern Recognition and 9 papers in Computational Mechanics. Recurrent topics in Leonardo Gabrielli's work include Speech and Audio Processing (28 papers), Music and Audio Processing (28 papers) and Music Technology and Sound Studies (20 papers). Leonardo Gabrielli is often cited by papers focused on Speech and Audio Processing (28 papers), Music and Audio Processing (28 papers) and Music Technology and Sound Studies (20 papers). Leonardo Gabrielli collaborates with scholars based in Italy, United Kingdom and United States. Leonardo Gabrielli's co-authors include Stefano Squartini, Francesco Piazza, Susanna Spinsante, Emanuele Principi, Fabio Vesperini, Marco Fagiani, Björn W. Schuller, Erik Marchi, Florian Eyben and Vesa Välimäki and has published in prestigious journals such as The Journal of the Acoustical Society of America, Sensors and Neurocomputing.

In The Last Decade

Leonardo Gabrielli

53 papers receiving 451 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Leonardo Gabrielli 189 156 97 87 55 57 470
Xinman Zhang 203 1.1× 228 1.5× 38 0.4× 115 1.3× 23 0.4× 87 528
Benayad Nsiri 111 0.6× 110 0.7× 56 0.6× 98 1.1× 10 0.2× 67 519
Di Zang 46 0.2× 160 1.0× 54 0.6× 60 0.7× 51 0.9× 44 507
M. Jaïdane-Saïdane 133 0.7× 69 0.4× 130 1.3× 47 0.5× 13 0.2× 80 439
Fengzhen Tang 85 0.4× 113 0.7× 60 0.6× 215 2.5× 21 0.4× 40 550
Suan Lee 50 0.3× 117 0.8× 28 0.3× 107 1.2× 26 0.5× 56 405
Renate Sitte 167 0.9× 60 0.4× 88 0.9× 113 1.3× 8 0.1× 46 483
Peng Wang 73 0.4× 129 0.8× 64 0.7× 98 1.1× 39 0.7× 59 460
Myung-Geun Chun 73 0.4× 102 0.7× 78 0.8× 97 1.1× 6 0.1× 58 529
Abderrezak Guessoum 88 0.5× 102 0.7× 165 1.7× 112 1.3× 55 1.0× 56 533

Countries citing papers authored by Leonardo Gabrielli

Since Specialization
Citations

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

Fields of papers citing papers by Leonardo Gabrielli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Leonardo Gabrielli

This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Gabrielli. A scholar is included among the top collaborators of Leonardo Gabrielli 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 Leonardo Gabrielli. Leonardo Gabrielli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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2026