Alexander Lerch

1.7k citations
66 papers · 852 · h-index 14

Impact in

Papers in

Alexander Lerch

59 papers receiving 789 citations

Peers

Alexander Lerch
Comparison fields: 5 of 90
  • Signal Processing 564
  • Computer Vision and Pattern Recognition 414
  • Music 54
  • Cognitive Neuroscience 163
  • Developmental Biology 15
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David Meredith Denmark
Joshua D. Reiss United Kingdom
Xavier Rodet France
James W. Beauchamp United States
Eric D. Scheirer United States
Beth Logan United States
Miller Puckette United States
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Gautham J. Mysore United States
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Citations per field
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Citations per year

Countries citing papers authored by Alexander Lerch

Since Specialization
Citations

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

Fields of papers citing papers by Alexander Lerch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Alexander Lerch, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alexander Lerch Line = papers co-authored together Alexander Lerch links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012110
2
An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics
201292
3 201888
4 202073
5
A HIERARCHICAL APPROACH TO AUTOMATIC MUSICAL GENRE CLASSIFICATION
200368
6
Hierarchical Automatic Audio Signal Classification
200446
7 201827
8 201824
9 201822
10 201518
11 202217
12 201715
13 201814
14 201513
15 202312
16 201812
17 200811
18 201110
19 201510
20
Objective Descriptors for the Assessment of Student Music Performances
201710

About Alexander Lerch

Alexander Lerch is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience and Music, having authored 66 papers that have together received 852 indexed citations. Recurring topics across this work include Music and Audio Processing (50 papers), Music Technology and Sound Studies (34 papers), Speech and Audio Processing (25 papers), Neuroscience and Music Perception (10 papers), Diverse Musicological Studies (5 papers), Speech Recognition and Synthesis (5 papers), Anomaly Detection Techniques and Applications (2 papers) and Evolutionary Algorithms and Applications (2 papers). The work is most often cited by research in Signal Processing (564 citations), Computer Vision and Pattern Recognition (414 citations), Music (54 citations), Cognitive Neuroscience (163 citations) and Developmental Biology (15 citations). Alexander Lerch has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Li-Chia Yang, Chih-Wei Wu, Juan José Burred, Weian Guo, Dongyang Li, Yongmei Li, Qidi Wu, Lei Wang, Holger Kirchhoff and Yihao Chen. Their work appears in journals such as Journal of the Audio Engineering Society, Applied Physics Letters, Swarm and Evolutionary Computation, Applied Sciences and IEEE Transactions on Cybernetics.

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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