Eric J. Humphrey

1.5k total citations
25 papers, 856 citations indexed

About

Eric J. Humphrey is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Eric J. Humphrey has authored 25 papers receiving a total of 856 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Signal Processing, 20 papers in Computer Vision and Pattern Recognition and 6 papers in Cognitive Neuroscience. Recurrent topics in Eric J. Humphrey's work include Music and Audio Processing (23 papers), Music Technology and Sound Studies (18 papers) and Speech and Audio Processing (11 papers). Eric J. Humphrey is often cited by papers focused on Music and Audio Processing (23 papers), Music Technology and Sound Studies (18 papers) and Speech and Audio Processing (11 papers). Eric J. Humphrey collaborates with scholars based in United States, France and Spain. Eric J. Humphrey's co-authors include Juan Pablo Bello, Brian McFee, Yann LeCun, Rachel Bittner, Oriol Nieto, Andreas Jansson, Aparna Kumar, Nicola Montecchio, Tillman Weyde and Justin Salamon and has published in prestigious journals such as IEEE Signal Processing Magazine, Computer Music Journal and Journal of Intelligent Information Systems.

In The Last Decade

Eric J. Humphrey

24 papers receiving 779 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eric J. Humphrey United States 13 767 446 224 130 68 25 856
Slim Essid France 15 534 0.7× 377 0.8× 178 0.8× 67 0.5× 45 0.7× 55 719
Sebastian Ewert Germany 13 794 1.0× 526 1.2× 126 0.6× 121 0.9× 50 0.7× 48 846
Jordi Pons Spain 11 427 0.6× 220 0.5× 148 0.7× 79 0.6× 46 0.7× 21 503
Jordi Bonada Spain 19 840 1.1× 491 1.1× 337 1.5× 178 1.4× 20 0.3× 71 921
Eric D. Scheirer United States 12 1.2k 1.6× 924 2.1× 203 0.9× 270 2.1× 22 0.3× 21 1.4k
Ryuichi Oka Japan 7 663 0.9× 527 1.2× 152 0.7× 109 0.8× 22 0.3× 58 824
Antti Eronen Finland 13 1.2k 1.5× 757 1.7× 249 1.1× 164 1.3× 33 0.5× 28 1.3k
Nicholas J. Bryan United States 16 356 0.5× 230 0.5× 109 0.5× 97 0.7× 22 0.3× 32 479
Giambattista Parascandolo Finland 7 444 0.6× 200 0.4× 187 0.8× 43 0.3× 13 0.2× 9 580
Jen-Yu Liu Taiwan 13 283 0.4× 210 0.5× 101 0.5× 89 0.7× 19 0.3× 28 443

Countries citing papers authored by Eric J. Humphrey

Since Specialization
Citations

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

Fields of papers citing papers by Eric J. Humphrey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric J. Humphrey. 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 Eric J. Humphrey. The network helps show where Eric J. Humphrey may publish in the future.

Co-authorship network of co-authors of Eric J. Humphrey

This figure shows the co-authorship network connecting the top 25 collaborators of Eric J. Humphrey. A scholar is included among the top collaborators of Eric J. Humphrey 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 Eric J. Humphrey. Eric J. Humphrey 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.
Humphrey, Eric J., Sravana Reddy, Prem Seetharaman, et al.. (2018). An Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the Effort to Automate the Understanding of Vocals in Music. IEEE Signal Processing Magazine. 36(1). 82–94. 23 indexed citations
2.
Humphrey, Eric J., et al.. (2018). OpenMIC-2018: An Open Data-set for Multiple Instrument Recognition. Zenodo (CERN European Organization for Nuclear Research). 438–444. 26 indexed citations
3.
Humphrey, Eric J., Nicola Montecchio, Rachel Bittner, Andreas Jansson, & Tristan Jehan. (2017). Mining Labeled Data From Web-Scale Collections For Vocal Activity Detection In Music.. Zenodo (CERN European Organization for Nuclear Research). 709–715. 4 indexed citations
4.
Bittner, Rachel, et al.. (2017). Automatic Playlist Sequencing And Transitions.. Zenodo (CERN European Organization for Nuclear Research). 442–448. 12 indexed citations
5.
Jansson, Andreas, Eric J. Humphrey, Nicola Montecchio, et al.. (2017). Singing Voice Separation With Deep U-Net Convolutional Networks.. City Research Online (City University London). 745–751. 191 indexed citations
6.
McFee, Brian, Eric J. Humphrey, & Julián Urbano. (2016). A plan for sustainable MIR evaluation. International Symposium/Conference on Music Information Retrieval. 285–291. 5 indexed citations
7.
Bittner, Rachel, Eric J. Humphrey, & Juan Pablo Bello. (2016). PySOX: Leveraging the Audio Signal Processing Power of SOX in Python. 12 indexed citations
8.
Humphrey, Eric J. & Juan Pablo Bello. (2015). Four Timely Insights On Automatic Chord Estimation.. Zenodo (CERN European Organization for Nuclear Research). 19 indexed citations
9.
McFee, Brian, Eric J. Humphrey, & Juan Pablo Bello. (2015). A Software Framework For Musical Data Augmentation.. Zenodo (CERN European Organization for Nuclear Research). 248–254. 52 indexed citations
10.
Raffel, Colin, Brian McFee, Eric J. Humphrey, et al.. (2014). Mir_Eval: A Transparent Implementation Of Common Mir Metrics.. Zenodo (CERN European Organization for Nuclear Research). 367–372. 190 indexed citations
11.
Humphrey, Eric J., et al.. (2014). Jams: A Json Annotated Music Specification For Reproducible Mir Research.. Zenodo (CERN European Organization for Nuclear Research). 591–596. 22 indexed citations
12.
Humphrey, Eric J., Oriol Nieto, & Juan Pablo Bello. (2013). Data Driven And Discriminative Projections For Large-Scale Cover Song Identification.. Zenodo (CERN European Organization for Nuclear Research). 149–154. 14 indexed citations
13.
Humphrey, Eric J., et al.. (2013). Demos and Late-Breaking Session of the Thirteenth International Society for Music Information Retrieval Conference (ISMIR 2012). Computer Music Journal. 37(2). 91–93. 1 indexed citations
14.
Nieto, Oriol, Eric J. Humphrey, & Juan Pablo Bello. (2012). Compressing Music Recordings into Audio Summaries.. International Symposium/Conference on Music Information Retrieval. 313–318. 3 indexed citations
15.
Humphrey, Eric J., Juan Pablo Bello, & Yann LeCun. (2012). Moving Beyond Feature Design: Deep Architectures And Automatic Feature Learning In Music Informatics.. Zenodo (CERN European Organization for Nuclear Research). 403–408. 74 indexed citations
16.
Humphrey, Eric J., et al.. (2012). Learning a robust Tonnetz-space transform for automatic chord recognition. 453–456. 33 indexed citations
17.
Humphrey, Eric J. & Juan Pablo Bello. (2012). Rethinking Automatic Chord Recognition with Convolutional Neural Networks. 357–362. 67 indexed citations
18.
Humphrey, Eric J., et al.. (2011). Non-Linear Semantic Embedding for Organizing Large Instrument Sample Libraries. 142–147. 9 indexed citations
19.
Humphrey, Eric J.. (2010). Automatic Characterization Of Digital Music For Rhythmic Auditory Stimulation.. Zenodo (CERN European Organization for Nuclear Research). 69–74. 1 indexed citations
20.
Humphrey, Eric J.. (1984). A vegetable with potential. Nutrition & Food Science. 84(2). 20–21. 1 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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