Emanuele Coviello

18 papers receiving 1.4k citations

Hit Papers

A new approach to cross-modal multimedia retrieval201020262015202020102013250500750

Peers

Emanuele Coviello
Comparison fields: 5 of 72
  • Computer Vision and Pattern Recognition 1.3k
  • Artificial Intelligence 444
  • Signal Processing 154
  • Information Systems 31
  • Cognitive Neuroscience 30
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Nikhil Rasiwasia United States
Xian-Sheng Hua China
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Emanuele Coviello relative to Nikhil Rasiwasia United States Nikhil Rasiwasia's profile →
Citations per field
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Citations per year

Countries citing papers authored by Emanuele Coviello

Since Specialization
Citations

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

Fields of papers citing papers by Emanuele Coviello

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emanuele Coviello

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

All Works

18 of 18 papers shown
#WorkIndexed citations
1 1
2
A scalable model for online contextual music recommendations
2
3 24
4
That was fast! Speeding up NN search of high dimensional distributions.
2
5
Understanding eye movements in face recognition with hidden Markov model
2
6 12
7
Understanding eye movements in face recognition with hidden Markov model - eScholarship
1
8
On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrievalbreakdown →
323
9
The variational hierarchical EM algorithm for clustering hidden Markov models
10
10 43
11 5
12 7
13 2
14 15
15
A new approach to cross-modal multimedia retrievalbreakdown →
948
16 8
17 25
18 39

About Emanuele Coviello

Emanuele Coviello is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Music, having authored 18 papers that have together received 1.5k indexed citations. Recurring topics across this work include Music and Audio Processing (11 papers), Video Analysis and Summarization (7 papers) and Music Technology and Sound Studies (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Signal Processing (154 citations) and Artificial Intelligence (444 citations). Emanuele Coviello has collaborated with scholars based in United States, Hong Kong and Italy. Frequent co-authors include Gert Lanckriet, José Costa Pereira, Roger Lévy, Nikhil Rasiwasia, Nuno Vasconcelos, Gabriel Doyle, Antoni B. Chan, Katherine Ellis, Janet H. Hsiao and Riccardo Miotto. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Cognitive Science and IEEE 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.

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