Peter Grosche

896 total citations
27 papers, 528 citations indexed

About

Peter Grosche is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Peter Grosche has authored 27 papers receiving a total of 528 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Signal Processing, 19 papers in Computer Vision and Pattern Recognition and 6 papers in Cognitive Neuroscience. Recurrent topics in Peter Grosche's work include Music and Audio Processing (23 papers), Speech and Audio Processing (19 papers) and Music Technology and Sound Studies (18 papers). Peter Grosche is often cited by papers focused on Music and Audio Processing (23 papers), Speech and Audio Processing (19 papers) and Music Technology and Sound Studies (18 papers). Peter Grosche collaborates with scholars based in Germany, Spain and Netherlands. Peter Grosche's co-authors include Meinard Müller, Sebastian Ewert, Joan Serrà, Frank Kurth, Stefan Weinzierl, Fabian Brinkmann, Josep Lluís Arcos, Craig Stuart Sapp, Frans Wiering and Steffen Lepa and has published in prestigious journals such as The Journal of the Acoustical Society of America, IEEE Transactions on Multimedia and IEEE Transactions on Audio Speech and Language Processing.

In The Last Decade

Peter Grosche

27 papers receiving 499 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Grosche Germany 12 470 355 157 43 27 27 528
Jordi Janer Spain 10 275 0.6× 177 0.5× 99 0.6× 48 1.1× 13 0.5× 43 333
Jordi Pons Spain 11 427 0.9× 220 0.6× 79 0.5× 148 3.4× 46 1.7× 21 503
Jouni Paulus Germany 12 544 1.2× 413 1.2× 127 0.8× 67 1.6× 18 0.7× 36 573
Sebastian Ewert Germany 13 794 1.7× 526 1.5× 121 0.8× 126 2.9× 50 1.9× 48 846
X. Rodet France 10 298 0.6× 189 0.5× 81 0.5× 39 0.9× 10 0.4× 20 364
Fabian-Robert Stöter Germany 10 473 1.0× 129 0.4× 80 0.5× 185 4.3× 9 0.3× 23 570
Christian Uhle Germany 9 308 0.7× 169 0.5× 90 0.6× 23 0.5× 8 0.3× 32 334
Rachel Bittner United States 13 645 1.4× 362 1.0× 124 0.8× 196 4.6× 51 1.9× 30 717
Erling Wold United States 6 558 1.2× 487 1.4× 102 0.6× 73 1.7× 34 1.3× 8 651
Chris Duxbury United Kingdom 9 738 1.6× 598 1.7× 200 1.3× 34 0.8× 26 1.0× 11 782

Countries citing papers authored by Peter Grosche

Since Specialization
Citations

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

Fields of papers citing papers by Peter Grosche

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Grosche

This figure shows the co-authorship network connecting the top 25 collaborators of Peter Grosche. A scholar is included among the top collaborators of Peter Grosche 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 Peter Grosche. Peter Grosche 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.
Serrà, Joan, Meinard Müller, Peter Grosche, & Josep Lluís Arcos. (2021). Unsupervised Detection of Music Boundaries by Time Series Structure Features. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 1613–1619. 5 indexed citations
2.
3.
Parviainen, Mikko, Pasi Pertilä, Tuomas Virtanen, & Peter Grosche. (2018). Time-Frequency Masking Strategies for Single-Channel Low-Latency Speech Enhancement Using Neural Networks. Trepo - Institutional Repository of Tampere University. abs 1412 6980. 51–55. 3 indexed citations
4.
Grosche, Peter, et al.. (2016). Extraction of anthropometric measures from 3D-meshes for the individualization of head-related transfer functions. Journal of the Audio Engineering Society. 5 indexed citations
5.
Geiger, Jürgen T., et al.. (2015). Dialogue enhancement of stereo sound. 2. 869–873. 8 indexed citations
6.
Serrà, Joan, Meinard Müller, Peter Grosche, & Josep Lluís Arcos. (2014). Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity. IEEE Transactions on Multimedia. 16(5). 1229–1240. 45 indexed citations
7.
Grosche, Peter, Meinard Müller, & Joan Serrà. (2013). Towards cover group thumbnailing. DIGITAL.CSIC (Spanish National Research Council (CSIC)). 613–616. 1 indexed citations
8.
Grosche, Peter & Meinard Müller. (2012). Toward musically-motivated audio fingerprints. 93–96. 4 indexed citations
9.
Grosche, Peter & Meinard Müller. (2012). Toward characteristic audio shingles for efficient cross-version music retrieval. 473–476. 15 indexed citations
10.
Müller, Meinard & Peter Grosche. (2012). Automated Segmentation of Folk Song Field Recordings. MPG.PuRe (Max Planck Society). 1–4. 3 indexed citations
11.
Grosche, Peter, Joan Serrà, Meinard Müller, & Josep Lluís Arcos. (2012). Structure-Based Audio Fingerprinting For Music Retrieval.. MPG.PuRe (Max Planck Society). 55–60. 8 indexed citations
12.
Müller, Meinard, et al.. (2012). A Robust Fitness Measure for Capturing Repetitions in Music Recordings With Applications to Audio Thumbnailing. IEEE Transactions on Audio Speech and Language Processing. 21(3). 531–543. 21 indexed citations
13.
Grosche, Peter, Meinard Müller, & Joan Serrà. (2012). Audio Content-Based Music Retrieval. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 28 indexed citations
14.
Grosche, Peter, et al.. (2011). A Re-Ordering Strategy For Accelerating Index-Based Audio Fingerprinting.. Max Planck Institute for Plasma Physics. 127–132. 4 indexed citations
15.
Müller, Meinard, et al.. (2011). A Segment-Based Fitness Measure For Capturing Repetitive Structures Of Music Recordings.. International Symposium/Conference on Music Information Retrieval. 258. 615–620. 13 indexed citations
16.
Müller, Meinard, Peter Grosche, & Frans Wiering. (2010). Automated analysis of performance variations in folk song recordings. Max Planck Institute for Plasma Physics. 247–256. 6 indexed citations
17.
Grosche, Peter, Meinard Müller, & Frank Kurth. (2010). Cyclic tempogram—A mid-level tempo representation for musicsignals. Max Planck Institute for Plasma Physics. 5522–5525. 52 indexed citations
18.
Grosche, Peter, Meinard Müller, & Craig Stuart Sapp. (2010). What Makes Beat Tracking Difficult? A Case Study On Chopin Mazurkas.. Zenodo (CERN European Organization for Nuclear Research). 649–654. 27 indexed citations
19.
Müller, Meinard, Peter Grosche, & Frans Wiering. (2009). Robust Segmentation And Annotation Of Folk Song Recordings.. Max Planck Institute for Plasma Physics. 735–740. 7 indexed citations
20.
Ewert, Sebastian, Meinard Müller, & Peter Grosche. (2009). High resolution audio synchronization using chroma onset features. Max Planck Institute for Plasma Physics. 1869–1872. 95 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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