Alexander Lerch
- Signal Processing top 1%
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Music top 2%
- Co-authors
- Li-Chia YangChih-Wei WuJuan José BurredWeian GuoYongmei LiLei WangQidi WuDongyang Li
- Topics
- Music and Audio Processing (50 papers)Music Technology and Sound Studies (34 papers)Speech and Audio Processing (25 papers)
- Partner nations
- United StatesGermanyAustria
In The Last Decade
Alexander Lerch
57 papers receiving 770 citations
Peers
Comparison fields: 5 of 93
- Signal Processing 556
- Computer Vision and Pattern Recognition 405
- Artificial Intelligence 208
- Cognitive Neuroscience 163
- Music 51
Countries citing papers authored by Alexander Lerch
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
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-authorship network of co-authors of Alexander Lerch
This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Lerch. A scholar is included among the top collaborators of Alexander Lerch 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 Alexander Lerch. Alexander Lerch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 17 | |
| 7 | 4 | |
| 8 | 71 | |
| 9 | 26 | |
| 10 | 22 | |
| 11 | 9 | |
| 12 | Multi-Track Crosstalk Reduction Using Spectral Subtraction | 0 |
| 13 | 15 | |
| 14 | 2 | |
| 15 | An Efficient Algorithm for Clipping Detection and Declipping Audio | 8 |
| 16 | 7 | |
| 17 | Genre-specific Key Profiles. | 1 |
| 18 | 8 | |
| 19 | FEAPI: A Low Level Feature Extraction Plugin API | 6 |
| 20 | Hierarchical Automatic Audio Signal Classification | 46 |
About Alexander Lerch
Alexander Lerch is a scholar working on Signal Processing, Music and Computer Vision and Pattern Recognition, having authored 66 papers that have together received 831 indexed citations. Recurring topics across this work include Music and Audio Processing (50 papers), Music Technology and Sound Studies (34 papers) and Speech and Audio Processing (25 papers). The work is most often cited by research in Signal Processing (556 citations), Computer Vision and Pattern Recognition (405 citations) and Music (51 citations). Alexander Lerch has collaborated with scholars based in United States, Germany and Austria. Frequent co-authors include Li-Chia Yang, Chih-Wei Wu, Juan José Burred, Weian Guo, Yongmei Li, Lei Wang, Qidi Wu, Dongyang Li, Holger Kirchhoff and Yihao Chen. Their work appears in journals such as Applied Physics Letters, The Journal of Physical Chemistry C and ACM Computing Surveys.
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.