Guillaume Lathoud

30 papers receiving 979 citations

Peers

Guillaume Lathoud
Comparison fields: 5 of 68
  • Signal Processing 615
  • Artificial Intelligence 572
  • Computer Vision and Pattern Recognition 326
  • Electrical and Electronic Engineering 147
  • Computational Mechanics 128
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Citations per field
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Countries citing papers authored by Guillaume Lathoud

Since Specialization
Citations

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

Fields of papers citing papers by Guillaume Lathoud

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guillaume Lathoud

This figure shows the co-authorship network connecting the top 25 collaborators of Guillaume Lathoud. A scholar is included among the top collaborators of Guillaume Lathoud 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 Guillaume Lathoud. Guillaume Lathoud 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
#WorkIndexed citations
1 20
2 13
3 10
4 27
5
Further Applications of Sector-Based Detection and Short-Term Clustering
2
6 5
7
Tracking People in Meetings with Particles
6
8 15
9 3
10
Multichannel Speech Enhancement in Cars: Explicit vs. Implicit Adaptation Control
2
11
The AMI meeting corpus
209
12 5
13
Unsupervised Location-Based Segmentation of Multi-Party Speech
20
14
A Sector-Based Approach for Localization of Multiple Speakers with Microphone Arrays
17
15
Automatic Analysis of Multimodal Group Actions in Meetings
91
16 17
17 84
18
Short-Term Spatio-Temporal Clustering of Sporadic and Concurrent Events
1
19
A Mixed-State I-Particle Filter for Multi-Camera Speaker Tracking
14
20 34

About Guillaume Lathoud

Guillaume Lathoud is a scholar working on Signal Processing, Computational Mechanics and Artificial Intelligence, having authored 31 papers that have together received 1.1k indexed citations. Recurring topics across this work include Speech and Audio Processing (27 papers), Music and Audio Processing (17 papers) and Advanced Adaptive Filtering Techniques (10 papers). The work is most often cited by research in Signal Processing (615 citations), Artificial Intelligence (572 citations) and Computer Vision and Pattern Recognition (326 citations). Guillaume Lathoud has collaborated with scholars based in Switzerland, Germany and Australia. Frequent co-authors include Iain McCowan, Daniel Gática-Pérez, Samy Bengio, Mark Barnard, Jean‐Marc Odobez, Mathew Magimai.-Doss, Pierre Wellner, Dong Zhang, H. Bourlard and David Moore. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Audio Speech and Language Processing and EURASIP Journal on Advances in Signal 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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