Andrew Horner

1.5k citations
124 papers · 1.2k · h-index 15

Impact in

Papers in

Andrew Horner

104 papers receiving 1.0k citations

Peers

Andrew Horner
Comparison fields: 5 of 88
  • Signal Processing 658
  • Computer Vision and Pattern Recognition 582
  • Cognitive Neuroscience 398
  • Polymers and Plastics 118
  • Artificial Intelligence 260
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Citations per year

Countries citing papers authored by Andrew Horner

Since Specialization
Citations

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

Fields of papers citing papers by Andrew Horner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Andrew Horner, 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 Andrew Horner Line = papers co-authored together Andrew Horner links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 124 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017226
2
Genetic Algorithms and Computer-Assisted Music Composition
1991110
3 199398
4 201482
5 201450
6
Methods for multiple wavetable synthesis of musical instrument tones
199333
7 199629
8 199624
9
Harmonization of Musical Progressions with Genetic Algorithms
199523
10 201021
11 199820
12 200419
13
Group Synthesis with Genetic Algorithms
199618
14 201515
15 201714
16 201414
17 201913
18 201413
19 199613
20
A Search for Best Error Metrics to Predict Discrimination of Original and Spectrally Altered Musical Instrument Sounds
200412

About Andrew Horner

Andrew Horner is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Artificial Intelligence and Automotive Engineering, having authored 124 papers that have together received 1.2k indexed citations. Recurring topics across this work include Music and Audio Processing (78 papers), Music Technology and Sound Studies (70 papers), Speech and Audio Processing (43 papers), Neuroscience and Music Perception (20 papers), Hearing Loss and Rehabilitation (15 papers), Vehicle Noise and Vibration Control (6 papers), Acoustic Wave Phenomena Research (6 papers) and Noise Effects and Management (5 papers). The work is most often cited by research in Signal Processing (658 citations), Computer Vision and Pattern Recognition (582 citations), Cognitive Neuroscience (398 citations), Polymers and Plastics (118 citations) and Artificial Intelligence (260 citations). Andrew Horner has collaborated with scholars based in Hong Kong, United States and Germany. Frequent co-authors include James W. Beauchamp, Bin Wu, David E. Goldberg, Lippold Haken, Qiang Yang, Erheng Zhong, Chung Lee, Richard H. Y. So, Xu Liu and Yue‐Feng Liu. Their work appears in journals such as Journal of the Audio Engineering Society, The Journal of the Acoustical Society of America, Computer Music Journal, IEEE Transactions on Speech and Audio Processing and Ergonomics.

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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