Tim Crawford

897 total citations
43 papers, 524 citations indexed

About

Tim Crawford is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Music. According to data from OpenAlex, Tim Crawford has authored 43 papers receiving a total of 524 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Signal Processing, 21 papers in Computer Vision and Pattern Recognition and 16 papers in Music. Recurrent topics in Tim Crawford's work include Music and Audio Processing (33 papers), Music Technology and Sound Studies (19 papers) and Diverse Musicological Studies (14 papers). Tim Crawford is often cited by papers focused on Music and Audio Processing (33 papers), Music Technology and Sound Studies (19 papers) and Diverse Musicological Studies (14 papers). Tim Crawford collaborates with scholars based in United Kingdom, United States and Austria. Tim Crawford's co-authors include Donald Byrd, Rajeev Raman, J. Stephen Downie, Jeremy Pickens, Costas S. Iliopoulos, Kevin Page, Geraínt A. Wiggins, Emilios Cambouropoulos, David De Roure and Christophe Rhodes and has published in prestigious journals such as SHILAP Revista de lepidopterología, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences and SAE technical papers on CD-ROM/SAE technical paper series.

In The Last Decade

Tim Crawford

36 papers receiving 437 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tim Crawford United Kingdom 14 417 336 147 104 98 43 524
Samer Abdallah United Kingdom 12 421 1.0× 296 0.9× 132 0.9× 62 0.6× 168 1.7× 26 570
Kjell Lemström Finland 12 353 0.8× 322 1.0× 119 0.8× 30 0.3× 64 0.7× 41 399
Christophe Rhodes United Kingdom 12 654 1.6× 540 1.6× 110 0.7× 64 0.6× 177 1.8× 30 726
Luís Gustavo Martins Portugal 8 383 0.9× 319 0.9× 102 0.7× 39 0.4× 121 1.2× 17 518
Andreas F. Ehmann United States 12 600 1.4× 428 1.3× 127 0.9× 51 0.5× 153 1.6× 24 661
Camilo Rueda Colombia 8 108 0.3× 129 0.4× 81 0.6× 9 0.1× 58 0.6× 27 237
Jia-Lien Hsu Taiwan 10 263 0.6× 231 0.7× 139 0.9× 16 0.2× 61 0.6× 36 412
Rainer Typke Netherlands 9 366 0.9× 338 1.0× 71 0.5× 33 0.3× 74 0.8× 12 417
Sergio Oramas Spain 10 279 0.7× 185 0.6× 212 1.4× 65 0.6× 41 0.4× 28 456
Ju-Chiang Wang Taiwan 14 374 0.9× 260 0.8× 136 0.9× 22 0.2× 130 1.3× 35 503

Countries citing papers authored by Tim Crawford

Since Specialization
Citations

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

Fields of papers citing papers by Tim Crawford

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tim Crawford

This figure shows the co-authorship network connecting the top 25 collaborators of Tim Crawford. A scholar is included among the top collaborators of Tim Crawford 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 Tim Crawford. Tim Crawford 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.
Crawford, Tim, et al.. (2019). JosquIntab: A Dataset for Content-based Computational Analysis of Music in Lute Tablature. Zenodo (CERN European Organization for Nuclear Research). 431–438. 2 indexed citations
2.
Crawford, Tim, et al.. (2018). Searching Page-Images of Early Music Scanned with OMR: A Scalable Solution Using Minimal Absent Words. International Symposium/Conference on Music Information Retrieval. 233–239. 3 indexed citations
3.
Lewis, David, et al.. (2017). On providing semantic alignment and unified access to music library metadata. International Journal on Digital Libraries. 20(1). 25–47. 10 indexed citations
4.
Lewis, David, Tim Crawford, & Daniel Müllensiefen. (2016). Instrumental Idiom In The 16Th Century: Embellishment Patterns In Arrangements Of Vocal Music.. Zenodo (CERN European Organization for Nuclear Research). 524–530.
5.
Lewis, Richard J., Tim Crawford, & David Lewis. (2015). Exploring information retrieval, semantic technologies and workflows for music scholarship: the Transforming Musicology project. Early Music. 43(4). 635–647. 3 indexed citations
6.
Crawford, Tim, et al.. (2014). Explorations in Linked Data practice for early music corpora. 309–312. 5 indexed citations
7.
Rhodes, Christophe, et al.. (2013). Breathy, Resonant, Pressed – Automatic Detection of Phonation Mode from Audio Recordings of Singing. Journal of New Music Research. 42(2). 171–186. 16 indexed citations
8.
Page, Kevin, et al.. (2013). Capturing the workflows of music information retrieval for repeatability and reuse. Journal of Intelligent Information Systems. 41(3). 435–459. 18 indexed citations
9.
Page, Kevin, et al.. (2012). Reuse, Remix, Repeat: The Workflows Of Mir.. Oxford University Research Archive (ORA) (University of Oxford). 409–414. 5 indexed citations
10.
Rhodes, Christophe, et al.. (2012). Breathy Or Resonant - A Controlled And Curated Dataset For Phonation Mode Detection In Singing.. Goldsmiths (University of London). 589–594. 1 indexed citations
11.
Roure, David De, et al.. (2011). An e-Research approach to Web-scale music analysis. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 369(1949). 3300–3317. 11 indexed citations
12.
Page, Kevin, et al.. (2010). Semantics for Music Analysis through Linked Data: How Country is My Country?. 41–48. 13 indexed citations
13.
Crawford, Tim, Matthias Mauch, & Christophe Rhodes. (2010). Recognising Classical Works In Historical Recordings.. Zenodo (CERN European Organization for Nuclear Research). 495–500. 4 indexed citations
14.
Wiggins, Geraínt A., et al.. (2007). How Many Beans Make Five? The Consensus Problem In Music-Genre Classification And A New Evaluation Method For Single-Genre Categorisation Systems.. Zenodo (CERN European Organization for Nuclear Research). 73–76. 14 indexed citations
15.
Clifford, Raphaël, Tim Crawford, Costas S. Iliopoulos, & David Meredith. (2004). Problems in Computational Musicology. Bristol Research (University of Bristol). 1 indexed citations
16.
Pickens, Jeremy, et al.. (2003). Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach. Journal of New Music Research. 32(2). 223–236. 41 indexed citations
17.
Pickens, Jeremy & Tim Crawford. (2002). Harmonic models for polyphonic music retrieval.
18.
Harwood, Ian & Tim Crawford. (2001). Angélique (i). Oxford Music Online.
19.
Crawford, Tim, et al.. (1998). String Matching Techniques for Musical Similarity and Melodic Recognition. Medical Entomology and Zoology. 11(11). 73–100. 87 indexed citations
20.
Crawford, Tim, et al.. (1994). Dynamic Testing and Evaluation of the Torsional Vibration Absorber. SAE technical papers on CD-ROM/SAE technical paper series. 1. 8 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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