Mathieu Lagrange

2.5k total citations · 1 hit paper
64 papers, 1.1k citations indexed

About

Mathieu Lagrange is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Speech and Hearing. According to data from OpenAlex, Mathieu Lagrange has authored 64 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Signal Processing, 31 papers in Computer Vision and Pattern Recognition and 12 papers in Speech and Hearing. Recurrent topics in Mathieu Lagrange's work include Music and Audio Processing (46 papers), Speech and Audio Processing (42 papers) and Music Technology and Sound Studies (27 papers). Mathieu Lagrange is often cited by papers focused on Music and Audio Processing (46 papers), Speech and Audio Processing (42 papers) and Music Technology and Sound Studies (27 papers). Mathieu Lagrange collaborates with scholars based in France, Canada and United Kingdom. Mathieu Lagrange's co-authors include Emmanouil Benetos, Mark D. Plumbley, Dimitrios Giannoulis, Dan Stowell, Toni Heittola, Tuomas Virtanen, Peter Foster, Annamaria Mesaros, George Tzanetakis and Arnaud Can and has published in prestigious journals such as PLoS ONE, Journal of Hydrology and The Journal of the Acoustical Society of America.

In The Last Decade

Mathieu Lagrange

58 papers receiving 1.1k citations

Hit Papers

Detection and Classification of Acoustic Scenes and Events 2015 2026 2018 2022 2015 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mathieu Lagrange France 15 866 401 246 127 124 64 1.1k
Karol J. Piczak Poland 3 1.1k 1.3× 417 1.0× 296 1.2× 265 2.1× 100 0.8× 4 1.3k
Christopher Jacoby Switzerland 3 656 0.8× 240 0.6× 180 0.7× 150 1.2× 79 0.6× 4 787
Qiuqiang Kong United Kingdom 22 1.4k 1.6× 709 1.8× 615 2.5× 113 0.9× 120 1.0× 62 1.8k
Toni Heittola Finland 23 2.4k 2.8× 1.1k 2.8× 758 3.1× 287 2.3× 188 1.5× 48 2.8k
Turab Iqbal United Kingdom 9 626 0.7× 321 0.8× 275 1.1× 63 0.5× 54 0.4× 11 832
Robert C. Maher United States 11 458 0.5× 274 0.7× 99 0.4× 78 0.6× 59 0.5× 59 654
Joan Claudi Socoró Spain 13 307 0.4× 82 0.2× 176 0.7× 34 0.3× 155 1.3× 55 607
Máximo Cobos Spain 20 906 1.0× 143 0.4× 155 0.6× 37 0.3× 218 1.8× 104 1.3k
Huy Dat Tran Singapore 14 350 0.4× 172 0.4× 209 0.8× 54 0.4× 49 0.4× 54 654
Katsutoshi Itoyama Japan 15 697 0.8× 233 0.6× 155 0.6× 16 0.1× 101 0.8× 127 874

Countries citing papers authored by Mathieu Lagrange

Since Specialization
Citations

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

Fields of papers citing papers by Mathieu Lagrange

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mathieu Lagrange

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

All Works

20 of 20 papers shown
1.
Lostanlen, Vincent, et al.. (2025). S-KEY: Self-supervised Learning of Major and Minor Keys from Audio. 1–5.
2.
Han, Han, Vincent Lostanlen, & Mathieu Lagrange. (2024). Learning to Solve Inverse Problems for Perceptual Sound Matching. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 2605–2615. 1 indexed citations
3.
Aumond, Pierre, et al.. (2024). Sound source classification for soundscape analysis using fast third-octave bands data from an urban acoustic sensor network. The Journal of the Acoustical Society of America. 156(1). 416–427. 2 indexed citations
4.
Benetos, Emmanouil, et al.. (2023). Perceptual Musical Similarity Metric Learning with Graph Neural Networks. 30. 1–5.
5.
Han, Han, Vincent Lostanlen, & Mathieu Lagrange. (2023). Perceptual–Neural–Physical Sound Matching. SPIRE - Sciences Po Institutional REpository. 1–5. 2 indexed citations
6.
Lagrange, Mathieu, et al.. (2023). Efficient bandwidth extension of musical signals using a differentiable harmonic plus noise model. EURASIP Journal on Audio Speech and Music Processing. 2023(1).
7.
Turchet, Luca, Mathieu Lagrange, Cristina Rottondi, et al.. (2023). The Internet of Sounds: Convergent Trends, Insights, and Future Directions. IEEE Internet of Things Journal. 10(13). 11264–11292. 47 indexed citations
8.
Lostanlen, Vincent, et al.. (2021). Energy Efficiency is Not Enough: Towards a Batteryless Internet of Sounds. SPIRE - Sciences Po Institutional REpository.
9.
Turchet, Luca, György Fazekas, Mathieu Lagrange, Hossein Shokri‐Ghadikolaei, & Carlo Fischione. (2020). The Internet of Audio Things: State of the Art, Vision, and Challenges. IEEE Internet of Things Journal. 7(10). 10233–10249. 50 indexed citations
10.
Rossignol, Mathias, et al.. (2018). Investigating the perception of soundscapes through acoustic scene simulation. Behavior Research Methods. 51(2). 532–555. 6 indexed citations
11.
Rossignol, Mathias, Mathieu Lagrange, & Arshia Cont. (2018). Efficient similarity-based data clustering by optimal object to cluster reallocation. PLoS ONE. 13(6). e0197450–e0197450. 5 indexed citations
12.
Can, Arnaud, et al.. (2017). Creation of a corpus of realistic urban sound scenes with controlled acoustic properties. Proceedings of meetings on acoustics. 55009–55009. 7 indexed citations
13.
Picaut, Judicaël, et al.. (2017). Characterization of urban sound environments using a comprehensive approach combining open data, measurements, and modeling. The Journal of the Acoustical Society of America. 141(5_Supplement). 3808–3808. 4 indexed citations
14.
Misdariis, Nicolas, et al.. (2016). Semantic Browsing of Sound Databases without Keywords. Journal of the Audio Engineering Society. 64(9). 628–635. 4 indexed citations
15.
Lagrange, Mathieu, et al.. (2015). Investigating soundscapes perception through acoustic scenes simulation. Université Pierre et Marie CURIE (UPMC). 1 indexed citations
16.
Lagrange, Mathieu, et al.. (2015). The bag-of-frames approach: A not so sufficient model for urban soundscapes. The Journal of the Acoustical Society of America. 138(5). EL487–EL492. 15 indexed citations
17.
Ozerov, Alexey, Mathieu Lagrange, & Emmanuel Vincent. (2011). GMM-based classification from noisy features. HAL (Le Centre pour la Communication Scientifique Directe). 8 indexed citations
18.
Lagrange, Mathieu, Gary Scavone, & Philippe Depalle. (2010). Analysis/Synthesis of Sounds Generated by Sustained Contact Between Rigid Objects. IEEE Transactions on Audio Speech and Language Processing. 18(3). 509–518. 5 indexed citations
19.
Lagrange, Mathieu, et al.. (2008). Normalized Cuts for Predominant Melodic Source Separation. IEEE Transactions on Audio Speech and Language Processing. 16(2). 278–290. 25 indexed citations
20.
Lagrange, Mathieu, Luís Gustavo Martins, & George Tzanetakis. (2007). Semi-Automatic Mono to Stereo Up-Mixing Using Sound Source Formation. Journal of the Audio Engineering Society. 3 indexed citations

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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