Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Arthur Lenoir

dblp:80/10575 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 67% Multimedia analysis and retrieval · 33%
Databases, data mining, and information retrieval
1 paper
Recommender systems · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing › music information retrieval
content-based music retrieval		 0.112011
MuMa: a scalable music search engine based on content analysis · ACM Multimedia 2011
Audio and music processing
music information retrieval		 0.112011
MuMa: a scalable music search engine based on content analysis · ACM Multimedia 2011
Multimedia analysis and retrieval › audio retrieval
music search engine		 0.112011
MuMa: a scalable music search engine based on content analysis · ACM Multimedia 2011
Recommender systems › music recommendation
playlist generation		 0.012011
MuMa: a scalable music search engine based on content analysis · ACM Multimedia 2011

Methods — techniques the papers use, named apart from their topics

content analysis · 0.2
YearPublicationVenuePosition
2011 MuMa: a scalable music search engine based on content analysis
abstract
Existing music search engines process music as they would process text, ignoring the richness of this media. We introduce here MuMa1 (MUsic MAshup), a web application which makes the most of audio contents (the signal itself) to provide the user with new ways of browsing music. MuMa allows the user to search for particular chords sequences, to generate smart playlists and to extend its musical universe.
Arthur Lenoir, Rémi Landais
 ACM Multimedia1