Ricard Marxer

2.1k total citations
66 papers, 719 citations indexed

About

Ricard Marxer is a scholar working on Signal Processing, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Ricard Marxer has authored 66 papers receiving a total of 719 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Signal Processing, 26 papers in Artificial Intelligence and 13 papers in Cognitive Neuroscience. Recurrent topics in Ricard Marxer's work include Speech and Audio Processing (32 papers), Music and Audio Processing (32 papers) and Speech Recognition and Synthesis (23 papers). Ricard Marxer is often cited by papers focused on Speech and Audio Processing (32 papers), Music and Audio Processing (32 papers) and Speech Recognition and Synthesis (23 papers). Ricard Marxer collaborates with scholars based in France, United Kingdom and Spain. Ricard Marxer's co-authors include Jon Barker, Shinji Watanabe, Emmanuel Vincent, Aditya Arie Nugraha, Steve Maddock, H.‐G. Purwins, Guy J. Brown, Frank Rudzicz, Jordi Janer and Perfecto Herrera and has published in prestigious journals such as PLoS ONE, Scientific Reports and The Journal of the Acoustical Society of America.

In The Last Decade

Ricard Marxer

63 papers receiving 666 citations

Peers

Ricard Marxer
Comparison fields: 5 of 74
  • Signal Processing 470
  • Artificial Intelligence 356
  • Cognitive Neuroscience 127
  • Computational Mechanics 101
  • Computer Vision and Pattern Recognition 100
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Citations per field, relative to Ricard Marxer
Ricard Marxer · 1×
Citations per year, relative to Ricard Marxer
Ricard Marxer · 1×

Countries citing papers authored by Ricard Marxer

Since Specialization
Citations

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

Fields of papers citing papers by Ricard Marxer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ricard Marxer

This figure shows the co-authorship network connecting the top 25 collaborators of Ricard Marxer. A scholar is included among the top collaborators of Ricard Marxer 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 Ricard Marxer. Ricard Marxer 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
# Work Indexed citations
1 0
2 1
3 1
4 3
5 1
6 4
7 1
8 16
9 1
10 9
11 4
12 6
13 71
14 29
15
Music classification using high-level models
2
16
Unsupervised Incremental Learning and Prediction of Audio Signals
2
17 5
18 21
19
Dynamical Hierarchical Self-Organization of Harmonic, Motivic, and Pitch Categories
11
20
What/when causal expectation modelling in monophonic pitched and percussive audio
2

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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