Justin London

2.9k citations
62 papers · 1.7k indexed · h-index 19

Justin London

55 papers receiving 1.5k citations

Peers

Justin London
Comparison fields: 5 of 98
  • Music 468
  • Cognitive Neuroscience 1.4k
  • Signal Processing 594
  • Developmental Biology 88
  • Experimental and Cognitive Psychology 389
Replace Eric Clarke with:
Eric Clarke United Kingdom
Elizabeth Hellmuth Margulis United States
Martin Clayton United Kingdom
Jamshed J. Bharucha United States
Dirk-Jan Povel Netherlands
Nori Jacoby United States
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Justin London relative to Eric Clarke United Kingdom Eric Clarke's profile →
Citations per field
00.5×2.8×
Eric Clarke · 1×
Citations per year

Countries citing papers authored by Justin London

Since Specialization
Citations

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

Fields of papers citing papers by Justin London

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20251
2 20242
3 20210
4 202115
5 202116
6 20213
7 201910
8 201914
9 201741
10 20179
11 201633
12 201617
13 201629
14 201516
15 201036
16 20091
17
Modeling Derivatives in C++ (+CD)
20042
18
Tonal Pitch Space (Book)
20021
19 19931
20
The interaction between meter and phrase beginnings and endings in the mature instrumental music of Haydn and Mozart
19902

About Justin London

Justin London is a scholar working on Music, Cognitive Neuroscience and Signal Processing, having authored 62 papers that have together received 1.7k indexed citations. Recurring topics across this work include Neuroscience and Music Perception (40 papers), Music Technology and Sound Studies (19 papers), Music and Audio Processing (19 papers), Musicology and Musical Analysis (10 papers), Hearing Loss and Rehabilitation (10 papers), Multisensory perception and integration (9 papers), Diverse Music Education Insights (8 papers) and Historical Linguistics and Language Studies (4 papers). The work is most often cited by research in Music (468 citations), Cognitive Neuroscience (1.4k citations) and Signal Processing (594 citations). Justin London has collaborated with scholars based in United States, Germany and Finland. Frequent co-authors include Rainer Polak, Bruno H. Repp, Peter E. Keller, Daniel J. Levitin, Jessica A. Grahn, Nori Jacoby, Marc R. Thompson, Birgitta Burger, Petri Toiviainen and Kristian Nymoen. Their work appears in journals such as Music Perception An Interdisciplinary Journal, Journal of Aesthetics and Art Criticism, Attention Perception & Psychophysics, Music Theory Online and Music Theory Spectrum.

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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