Maarten Grachten

989 total citations
43 papers, 470 citations indexed

About

Maarten Grachten is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Maarten Grachten has authored 43 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Signal Processing, 38 papers in Computer Vision and Pattern Recognition and 27 papers in Cognitive Neuroscience. Recurrent topics in Maarten Grachten's work include Music and Audio Processing (39 papers), Music Technology and Sound Studies (37 papers) and Neuroscience and Music Perception (27 papers). Maarten Grachten is often cited by papers focused on Music and Audio Processing (39 papers), Music Technology and Sound Studies (37 papers) and Neuroscience and Music Perception (27 papers). Maarten Grachten collaborates with scholars based in Austria, Spain and Sri Lanka. Maarten Grachten's co-authors include Gerhard Widmer, Josep Lluís Arcos, Ramón López de Mántaras, Werner Goebl, Martin Gasser, Andreas Arzt, Xavier Serra, Marc Leman, Ricard Marxer and H.‐G. Purwins and has published in prestigious journals such as SHILAP Revista de lepidopterología, Machine Learning and Applied Sciences.

In The Last Decade

Maarten Grachten

42 papers receiving 423 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maarten Grachten Austria 15 366 365 265 46 43 43 470
Cyril Laurier Spain 8 270 0.7× 424 1.2× 183 0.7× 37 0.8× 109 2.5× 18 481
Óscar Mayor Spain 8 259 0.7× 324 0.9× 132 0.5× 46 1.0× 44 1.0× 19 401
Nicolas Wack Spain 10 373 1.0× 477 1.3× 116 0.4× 41 0.9× 65 1.5× 20 536
Oded Ben‐Tal United Kingdom 9 143 0.4× 116 0.3× 119 0.4× 33 0.7× 25 0.6× 18 240
Cynthia C. S. Liem Netherlands 11 236 0.6× 250 0.7× 92 0.3× 52 1.1× 82 1.9× 65 411
Christopher Ariza United States 8 189 0.5× 190 0.5× 109 0.4× 34 0.7× 36 0.8× 15 262
Cory McKay Canada 14 568 1.6× 605 1.7× 232 0.9× 101 2.2× 111 2.6× 32 754
Sankalp Gulati Spain 12 404 1.1× 537 1.5× 206 0.8× 65 1.4× 58 1.3× 26 579
Jeffrey J. Scott United States 7 183 0.5× 286 0.8× 126 0.5× 16 0.3× 60 1.4× 12 345
Raymond Migneco United States 6 158 0.4× 248 0.7× 118 0.4× 15 0.3× 56 1.3× 13 318

Countries citing papers authored by Maarten Grachten

Since Specialization
Citations

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

Fields of papers citing papers by Maarten Grachten

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maarten Grachten

This figure shows the co-authorship network connecting the top 25 collaborators of Maarten Grachten. A scholar is included among the top collaborators of Maarten Grachten 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 Maarten Grachten. Maarten Grachten 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.
Grachten, Maarten, et al.. (2022). On the Development and Practice of AI Technology for Contemporary Popular Music Production. SHILAP Revista de lepidopterología. 5(1). 35–35. 16 indexed citations
2.
Grachten, Maarten, et al.. (2020). BassNet: A Variational Gated Autoencoder for Conditional Generation of Bass Guitar Tracks with Learned Interactive Control. Applied Sciences. 10(18). 6627–6627. 10 indexed citations
3.
Grachten, Maarten, et al.. (2018). A Predictive Model for Music Based on Learned Interval Representations. arXiv (Cornell University). 26–33. 1 indexed citations
4.
Grachten, Maarten, et al.. (2017). From Bach To The Beatles: The Simulation Of Human Tonal Expectation Using Ecologically-Trained Predictive Models.. Zenodo (CERN European Organization for Nuclear Research). 494–501. 1 indexed citations
5.
Widmer, Gerhard, et al.. (2017). An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music. Machine Learning. 106(6). 887–909. 16 indexed citations
6.
Weyde, Tillman, et al.. (2016). Composer Recognition Based On 2D-Filtered Piano-Rolls.. VBN Forskningsportal (Aalborg Universitet). 115–121. 3 indexed citations
7.
Grachten, Maarten, et al.. (2015). Pseudo-supervised training improves unsupervised melody segmentation. International Conference on Artificial Intelligence. 2459–2465. 1 indexed citations
8.
Arzt, Andreas, et al.. (2015). Artificial intelligence in the concertgebouw. International Conference on Artificial Intelligence. 2424–2430. 11 indexed citations
9.
Grachten, Maarten, et al.. (2014). Developing Tonal Perception Through Unsupervised Learning.. Zenodo (CERN European Organization for Nuclear Research). 195–200. 5 indexed citations
10.
Grachten, Maarten, et al.. (2014). Predicting Expressive Dynamics In Piano Performances Using Neural Networks.. Zenodo (CERN European Organization for Nuclear Research). 45–52. 4 indexed citations
11.
Grachten, Maarten, Martin Gasser, Andreas Arzt, & Gerhard Widmer. (2013). Automatic Alignment Of Music Performances With Structural Differences.. Zenodo (CERN European Organization for Nuclear Research). 607–612. 28 indexed citations
12.
Grachten, Maarten, et al.. (2011). Toward E-Motion-Based Music Retrieval a Study of Affective Gesture Recognition. IEEE Transactions on Affective Computing. 3(2). 250–259. 13 indexed citations
13.
Grachten, Maarten, et al.. (2009). Expressive Performance Rendering: Introducing Performance Context. Zenodo (CERN European Organization for Nuclear Research). 9 indexed citations
14.
Grachten, Maarten, Markus Schedl, Tim Pohle, & Gerhard Widmer. (2009). The Ismir Cloud: A Decade Of Ismir Conferences At Your Fingertips.. International Symposium/Conference on Music Information Retrieval. 13(1). 63–68. 15 indexed citations
15.
Grachten, Maarten, et al.. (2009). Phase-plane Representation and Visualization of Gestural Structure in Expressive Timing. Journal of New Music Research. 38(2). 183–195. 8 indexed citations
16.
Purwins, H.‐G., et al.. (2008). Computational models of music perception and cognition I: The perceptual and cognitive processing chain. Physics of Life Reviews. 5(3). 151–168. 21 indexed citations
17.
Purwins, H.‐G., et al.. (2008). Computational models of music perception and cognition II: Domain-specific music processing. Physics of Life Reviews. 5(3). 169–182. 20 indexed citations
18.
Grachten, Maarten, Josep Lluís Arcos, & Ramón López de Mántaras. (2006). A case based approach to expressivity-aware tempo transformation. Machine Learning. 65(2-3). 411–437. 14 indexed citations
19.
Grachten, Maarten, Josep Lluís Arcos, & Ramón López de Mántaras. (2005). Melody retrieval using the Implication/Realization Model. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 22 indexed citations
20.
Grachten, Maarten & Josep Lluís Arcos. (2004). Using the Implication/Realization model for measuring melodic similarity. European Conference on Artificial Intelligence. 1023–1024. 1 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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