Graham E. Poliner

1.2k total citations
9 papers, 718 citations indexed

About

Graham E. Poliner is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Cognitive Neuroscience. According to data from OpenAlex, Graham E. Poliner has authored 9 papers receiving a total of 718 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 8 papers in Signal Processing and 1 paper in Cognitive Neuroscience. Recurrent topics in Graham E. Poliner's work include Music Technology and Sound Studies (8 papers), Music and Audio Processing (8 papers) and Speech and Audio Processing (6 papers). Graham E. Poliner is often cited by papers focused on Music Technology and Sound Studies (8 papers), Music and Audio Processing (8 papers) and Speech and Audio Processing (6 papers). Graham E. Poliner collaborates with scholars based in United States, Spain and Canada. Graham E. Poliner's co-authors include Daniel P. W. Ellis, Michael Mandel, Sebastian Streich, Emília Gómez, Andreas F. Ehmann, Charlie K. Dagli, Edward Yi Chang, S. Rajaram, Thomas S. Huang and Juan Pablo Bello and has published in prestigious journals such as Proceedings of the IEEE, Machine Learning and IEEE Transactions on Audio Speech and Language Processing.

In The Last Decade

Graham E. Poliner

9 papers receiving 638 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Graham E. Poliner United States	8	610	554	132	71	37	9	718
Andreas F. Ehmann United States	12	600 1.0×	428 0.8×	127 1.0×	153 2.2×	51 1.4×	24	661
Rainer Typke Netherlands	9	366 0.6×	338 0.6×	71 0.5×	74 1.0×	33 0.9×	12	417
Mert Bay United States	8	316 0.5×	204 0.4×	70 0.5×	90 1.3×	29 0.8×	13	347
Adam Berenzweig United States	9	547 0.9×	418 0.8×	151 1.1×	89 1.3×	30 0.8×	9	631
Douglas Keislar United States	7	561 0.9×	505 0.9×	73 0.6×	68 1.0×	27 0.7×	12	632
Nicholas J. Bryan United States	16	356 0.6×	230 0.4×	109 0.8×	97 1.4×	22 0.6×	32	479
Ju-Chiang Wang Taiwan	14	374 0.6×	260 0.5×	136 1.0×	130 1.8×	22 0.6×	35	503
Siddharth Sigtia United Kingdom	9	385 0.6×	262 0.5×	116 0.9×	50 0.7×	40 1.1×	13	431
Thom Blum United States	6	539 0.9×	477 0.9×	72 0.5×	65 0.9×	14 0.4×	9	626
Christian Dittmar Germany	12	462 0.8×	309 0.6×	91 0.7×	60 0.8×	28 0.8×	61	496

Countries citing papers authored by Graham E. Poliner

Since Specialization

Citations

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

Fields of papers citing papers by Graham E. Poliner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Graham E. Poliner. 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 Graham E. Poliner. The network helps show where Graham E. Poliner may publish in the future.

Co-authorship network of co-authors of Graham E. Poliner

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

All Works

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9 of 9 papers shown

1.

Huang, Thomas S., Charlie K. Dagli, S. Rajaram, et al.. (2008). Active Learning for Interactive Multimedia Retrieval. Proceedings of the IEEE. 96(4). 648–667. 69 indexed citations

2.

Poliner, Graham E., et al.. (2007). Melody Transcription From Music Audio: Approaches and Evaluation. IEEE Transactions on Audio Speech and Language Processing. 15(4). 1247–1256. 129 indexed citations

3.

Poliner, Graham E. & Daniel P. W. Ellis. (2007). Improving Generalization for Classification-Based Polyphonic Piano Transcription. 1. 86–89. 6 indexed citations

4.

Ellis, Daniel P. W. & Graham E. Poliner. (2007). Identifying `Cover Songs' with Chroma Features and Dynamic Programming Beat Tracking. Columbia Academic Commons (Columbia University). IV–1429. 221 indexed citations

5.

Mandel, Michael, Graham E. Poliner, & Daniel P. W. Ellis. (2006). Support vector machine active learning for music retrieval. Multimedia Systems. 12(1). 3–13. 83 indexed citations

6.

Gómez, Emília, Sebastian Streich, Rui Pedro Paiva, et al.. (2006). A Quantitative Comparison of Different Approaches for Melody Extraction from Polyphonic Audio Recordings. 9 indexed citations

7.

Poliner, Graham E. & Daniel P. W. Ellis. (2006). A Discriminative Model for Polyphonic Piano Transcription. EURASIP Journal on Advances in Signal Processing. 2007(1). 142 indexed citations

8.

Ellis, Daniel P. W. & Graham E. Poliner. (2006). Classification-based melody transcription. Machine Learning. 65(2-3). 439–456. 31 indexed citations

9.

Poliner, Graham E. & Daniel P. W. Ellis. (2005). A Classification Approach to Melody Transcription. Columbia Academic Commons (Columbia University). 161–166. 28 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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