Martin Rohrmeier

3.2k total citations
81 papers, 1.8k citations indexed

About

Martin Rohrmeier is a scholar working on Cognitive Neuroscience, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Martin Rohrmeier has authored 81 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 53 papers in Cognitive Neuroscience, 50 papers in Signal Processing and 29 papers in Computer Vision and Pattern Recognition. Recurrent topics in Martin Rohrmeier's work include Neuroscience and Music Perception (50 papers), Music and Audio Processing (49 papers) and Music Technology and Sound Studies (28 papers). Martin Rohrmeier is often cited by papers focused on Neuroscience and Music Perception (50 papers), Music and Audio Processing (49 papers) and Music Technology and Sound Studies (28 papers). Martin Rohrmeier collaborates with scholars based in Germany, Switzerland and United Kingdom. Martin Rohrmeier's co-authors include Stefan Koelsch, Patrick Rebuschat, Hugo Merchant, Jessica A. Grahn, W. Tecumseh Fitch, Laurel J. Trainor, Sebastian Jentschke, Moritz Lehne, Ian Cross and Marcus T. Pearce and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Martin Rohrmeier

75 papers receiving 1.7k citations

Peers

Martin Rohrmeier

ECP

CVPR

Martin Clayton United Kingdom

Karla K. Evans United States

John Laver United Kingdom

Anders Eriksson Sweden

Grant Fairbanks United States

Peter Roach United Kingdom

Robert F. Port United States

Benjamin W. White United States

Xing Tian China

Anne Danielsen Norway

Martin Clayton United Kingdom

Martin Rohrmeier

682 ×0.5

266 ×0.4

166 ×0.4

ECP

249 ×0.8

CVPR

317 ×1.3

Citations per year, relative to Martin Rohrmeier Martin Rohrmeier (= 1×) peers Martin Clayton

Countries citing papers authored by Martin Rohrmeier

Since Specialization

Citations

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

Fields of papers citing papers by Martin Rohrmeier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Martin Rohrmeier

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

All Works

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20 of 20 papers shown

Rohrmeier, Martin, et al.. (2025). A corpus and a modular infrastructure for the empirical study of (an)notated music. Scientific Data. 12(1). 685–685.

Rohrmeier, Martin, et al.. (2024). An Annotated Corpus of Tonal Piano Music from the Long 19th Century. SHILAP Revista de lepidopterología. 18(1). 84–95. 2 indexed citations

Rohrmeier, Martin, et al.. (2024). Computational modeling of interval distributions in tonal space reveals paradigmatic stylistic changes in Western music history. Humanities and Social Sciences Communications. 11(1).

Rohrmeier, Martin, et al.. (2023). ms3: A parser for MuseScore files, serving as datafactory for annotated music corpora. The Journal of Open Source Software. 8(88). 5195–5195. 4 indexed citations

Herff, Steffen A., et al.. (2023). Interpreting Rhythm as Parsing: Syntactic‐Processing Operations Predict the Migration of Visual Flashes as Perceived During Listening to Musical Rhythms. Cognitive Science. 47(12). e13389–e13389. 1 indexed citations

Rohrmeier, Martin, et al.. (2022). The line of fifths and the co-evolution of tonal pitch-classes. Journal of Mathematics and Music. 17(2). 173–197. 3 indexed citations

Rohrmeier, Martin, et al.. (2019). Statistical characteristics of tonal harmony: A corpus study of Beethoven’s string quartets. PLoS ONE. 14(6). e0217242–e0217242. 25 indexed citations

Kringelbach, Morten L., et al.. (2019). Pessimistic outcome expectancy does not explain ambiguity aversion in decision-making under uncertainty. Scientific Reports. 9(1). 12177–12177. 6 indexed citations

Cross, Ian, et al.. (2017). Sensory cortical response to uncertainty and low salience during recognition of affective cues in musical intervals. PLoS ONE. 12(4). e0175991–e0175991. 4 indexed citations

10.

Koelsch, Stefan, Tobias Busch, Sebastian Jentschke, & Martin Rohrmeier. (2016). Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences. Scientific Reports. 6(1). 19741–19741. 53 indexed citations

11.

Wiggins, Geraínt A., Peter L. Tyack, Constance Scharff, & Martin Rohrmeier. (2015). The evolutionary roots of creativity: mechanisms and motivations. Philosophical Transactions of the Royal Society B Biological Sciences. 370(1664). 20140099–20140099. 34 indexed citations

12.

Rohrmeier, Martin & Ian Cross. (2014). Modelling unsupervised online-learning of artificial grammars: Linking implicit and statistical learning. Consciousness and Cognition. 27. 155–167. 12 indexed citations

13.

Rohrmeier, Martin & Thore Graepel. (2012). Comparing Feature-Based Models of Harmony. 12 indexed citations

14.

Zeitler, Katharina, Stephan Schwarz‐Furlan, Abbas Agaimy, et al.. (2012). Aberrations of MET are associated with copy number gain of EGFR and loss of PTEN and predict poor outcome in patients with salivary gland cancer. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 462(1). 65–72. 23 indexed citations

15.

Rohrmeier, Martin & Stefan Koelsch. (2012). Predictive information processing in music cognition. A critical review. International Journal of Psychophysiology. 83(2). 164–175. 136 indexed citations

16.

Rebuschat, Patrick, et al.. (2011). Introduction: Language and music as cognitive systems. Lancaster EPrints (Lancaster University). 1 indexed citations

17.

Rohrmeier, Martin, Patrick Rebuschat, & Ian Cross. (2010). Incidental and online learning of melodic structure. Consciousness and Cognition. 20(2). 214–222. 55 indexed citations

18.

Rohrmeier, Martin, et al.. (2010). Syndrom des dehiszenten superioren Bogengangs. HNO. 58(10). 1057–1060. 4 indexed citations

19.

Haas, Wolfgang, Martin Rohrmeier, Remco C. Veltkamp, & Frans Wiering. (2009). MODELING HARMONIC SIMILARITY USING A GENERATIVE GRAMMAR OF TONAL HARMONY. International Symposium/Conference on Music Information Retrieval. 549–554. 22 indexed citations

20.

Rohrmeier, Martin, et al.. (2008). Implicit learning of phrase-structure grammar in language and music. Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences). 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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