Cyril Laurier

739 total citations
18 papers, 481 citations indexed

About

Cyril Laurier is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Music. According to data from OpenAlex, Cyril Laurier has authored 18 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Signal Processing, 10 papers in Computer Vision and Pattern Recognition and 4 papers in Music. Recurrent topics in Cyril Laurier's work include Music and Audio Processing (14 papers), Music Technology and Sound Studies (10 papers) and Speech and Audio Processing (7 papers). Cyril Laurier is often cited by papers focused on Music and Audio Processing (14 papers), Music Technology and Sound Studies (10 papers) and Speech and Audio Processing (7 papers). Cyril Laurier collaborates with scholars based in Spain, Germany and Italy. Cyril Laurier's co-authors include Perfecto Herrera, Jens Grivolla, Mohamed Sordo, Joan Serrà, J. Stephen Downie, Andreas F. Ehmann, Mert Bay, Xiao Hu, Òscar Celma and Claudiu S. Firan and has published in prestigious journals such as Multimedia Tools and Applications, International Symposium/Conference on Music Information Retrieval and Zenodo (CERN European Organization for Nuclear Research).

In The Last Decade

Cyril Laurier

16 papers receiving 433 citations

Peers

Countries citing papers authored by Cyril Laurier

Since Specialization

Citations

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

Fields of papers citing papers by Cyril Laurier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cyril Laurier

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

All Works

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18 of 18 papers shown

#	Work	Indexed citations
1	Support Vector Machine Active Learning for Music Mood Tagging 2012 ·(unknown), Cyril Laurier, Perfecto Herrera	2
2	Music classification using high-level models 2010 ·Nicolas Wack, Cyril Laurier, (unknown), Ricard Marxer, Dmitry Bogdanov, Joan Serrà, Emília Gómez, Perfecto Herrera	2
3	QueryBag: Using Different Sources For Querying Large Music Collections 2009 ·Mohamed Sordo, Òscar Celma, Cyril Laurier	2
4	Unsupervised Detection Of Cover Song Sets: Accuracy Improvement And Original Identification. 2009 ·Zenodo (CERN European Organization for Nuclear Research) ·Joan Serrà, Massimiliano Zanin, Cyril Laurier, Mohamed Sordo	2
5	Mood Cloud 2.0: Music Mood Browsing based on Social Networks 2009 ·Cyril Laurier, Mohamed Sordo, Perfecto Herrera	6
6	Music Mood Representations From Social Tags. 2009 ·Zenodo (CERN European Organization for Nuclear Research) ·Cyril Laurier, Mohamed Sordo, Joan Serrà, Perfecto Herrera	64
7	Music Mood And Theme Classification - A Hybrid Approach. 2009 ·Zenodo (CERN European Organization for Nuclear Research) ·Kerstin Bischoff, Claudiu S. Firan, Raluca Paiu, Wolfgang Nejdl, Cyril Laurier, Mohamed Sordo	54
8	MUSIC TYPE GROUPERS (MTG): GENERIC MUSIC CLASSIFICATION ALGORITHMS 2009 ·Nicolas Wack, (unknown), Cyril Laurier, (unknown), Ricard Marxer, Dmitry Bogdanov, Joan Serrà, Perfecto Herrera	4
9	Pharos 2009 ·Alessandro Bozzon, Marco Brambilla, Piero Fraternali, (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), (unknown), Cyril Laurier, (unknown), (unknown), Daniel Weinland, Vincenzo Croce	2
10	The Discipline formerly known as MIR 2009 ·(unknown), (unknown), Cyril Laurier, (unknown), (unknown), Xavier Serra	4
11	Pharos: An Audiovisual Search Platform using Music Information Retrieval Techniques 2009 ·Cyril Laurier, Mohamed Sordo, Alessandro Bozzon, Marco Brambilla, Piero Fraternali	1
12	Music Mood Annotator Design and Integration 2009 ·Cyril Laurier, (unknown), Joan Serrà, (unknown), Perfecto Herrera	13
13	Indexing music by mood: design and integration of an automatic content-based annotator 2009 ·Multimedia Tools and Applications ·Cyril Laurier, (unknown), Joan Serrà, (unknown), Perfecto Herrera, Xavier Serra	33
14	The 2007 MIREX Audio Mood Classification Task: Lessons Learned 2008 ·International Symposium/Conference on Music Information Retrieval ·Xiao Hu, J. Stephen Downie, Cyril Laurier, Mert Bay, Andreas F. Ehmann	110
15	Mood Cloud : A Real-Time Music Mood Visualization Tool 2008 ·Cyril Laurier, Perfecto Herrera	8
16	Multimodal Music Mood Classification Using Audio and Lyrics 2008 ·Cyril Laurier, Jens Grivolla, Perfecto Herrera	141
17	Annotating Music Collections: How Content-Based Similarity Helps To Propagate Labels. 2007 ·Zenodo (CERN European Organization for Nuclear Research) ·Mohamed Sordo, Cyril Laurier, Òscar Celma	33
18	Attributs multiples dans un improvisateur automatique 2004 ·Cyril Laurier	0

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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