Jeffrey J. Scott

534 total citations
12 papers, 345 citations indexed

About

Jeffrey J. Scott is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Jeffrey J. Scott has authored 12 papers receiving a total of 345 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Signal Processing, 10 papers in Computer Vision and Pattern Recognition and 2 papers in Cognitive Neuroscience. Recurrent topics in Jeffrey J. Scott's work include Music and Audio Processing (11 papers), Music Technology and Sound Studies (10 papers) and Speech and Audio Processing (7 papers). Jeffrey J. Scott is often cited by papers focused on Music and Audio Processing (11 papers), Music Technology and Sound Studies (10 papers) and Speech and Audio Processing (7 papers). Jeffrey J. Scott collaborates with scholars based in United States. Jeffrey J. Scott's co-authors include Youngmoo E. Kim, Erik M. Schmidt, Raymond Migneco, Brandon G. Morton, Patrick Richardson, Douglas Turnbull, Sang Won Lee and Brian Dolhansky and has published in prestigious journals such as International Symposium/Conference on Music Information Retrieval, Digital Collections - Ithaca College Library (Ithaca College) and Zenodo (CERN European Organization for Nuclear Research).

In The Last Decade

Jeffrey J. Scott

11 papers receiving 303 citations

Peers

Jeffrey J. Scott
Raymond Migneco United States
Patrick Richardson United States
Renato Panda Portugal
Óscar Mayor Spain
Ricardo Malheiro Portugal
Danning Jiang China
Cyril Laurier Spain
Anna Aljanaki Netherlands
Estefanía Cano Germany
Oded Ben‐Tal United Kingdom
Raymond Migneco United States
Jeffrey J. Scott
Citations per year, relative to Jeffrey J. Scott Jeffrey J. Scott (= 1×) peers Raymond Migneco

Countries citing papers authored by Jeffrey J. Scott

Since Specialization
Citations

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

Fields of papers citing papers by Jeffrey J. Scott

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jeffrey J. Scott

This figure shows the co-authorship network connecting the top 25 collaborators of Jeffrey J. Scott. A scholar is included among the top collaborators of Jeffrey J. Scott 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 Jeffrey J. Scott. Jeffrey J. Scott is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Lee, Sang Won & Jeffrey J. Scott. (2017). Word level lyrics-audio synchronization using separated vocals. 646–650. 9 indexed citations
2.
Scott, Jeffrey J., et al.. (2014). Representing Musical Patterns via the Rhythmic Style Histogram Feature. 1057–1060. 2 indexed citations
3.
Schmidt, Erik M., et al.. (2013). Toward Understanding Expressive Percussion Through Content Based Analysis.. Zenodo (CERN European Organization for Nuclear Research). 143–148. 4 indexed citations
4.
Scott, Jeffrey J. & Youngmoo E. Kim. (2013). Instrument Identification Informed Multi-Track Mixing.. Zenodo (CERN European Organization for Nuclear Research). 305–310. 7 indexed citations
5.
Schmidt, Erik M., Jeffrey J. Scott, & Youngmoo E. Kim. (2012). Feature Learning In Dynamic Environments: Modeling The Acoustic Structure Of Musical Emotion.. Zenodo (CERN European Organization for Nuclear Research). 325–330. 21 indexed citations
6.
Scott, Jeffrey J. & Youngmoo E. Kim. (2011). ANALYSIS OF ACOUSTIC FEATURES FOR AUTOMATED MULTI-TRACK MIXING. International Symposium/Conference on Music Information Retrieval. 621–626. 10 indexed citations
7.
Schmidt, Erik M., Raymond Migneco, Jeffrey J. Scott, & Youngmoo E. Kim. (2011). Modeling musical instrument tones as dynamic textures. 329–332. 2 indexed citations
8.
Kim, Youngmoo E., Raymond Migneco, Patrick Richardson, et al.. (2011). Teaching STEM concepts through Music Technology and DSP. 220–225. 12 indexed citations
9.
Kim, Youngmoo E., Erik M. Schmidt, Raymond Migneco, et al.. (2010). Music emotion recognition: A state of the art review. Digital Collections - Ithaca College Library (Ithaca College). 255. 239 indexed citations
10.
Scott, Jeffrey J., et al.. (2010). An Audio Processing Library For Mir Application Development In Flash.. Zenodo (CERN European Organization for Nuclear Research). 643–648. 1 indexed citations
11.
Kim, Youngmoo E., Erik M. Schmidt, Raymond Migneco, et al.. (2010). State Of The Art Report: Music Emotion Recognition: A State Of The Art Review.. Zenodo (CERN European Organization for Nuclear Research). 255–266. 33 indexed citations
12.
Migneco, Raymond, et al.. (2009). An audio processing library for game development in flash. 305. 201–209. 5 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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