Paul Lamere

2.5k total citations · 1 hit paper
28 papers, 1.5k citations indexed

About

Paul Lamere is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Paul Lamere has authored 28 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Signal Processing, 18 papers in Computer Vision and Pattern Recognition and 9 papers in Artificial Intelligence. Recurrent topics in Paul Lamere's work include Music and Audio Processing (21 papers), Music Technology and Sound Studies (16 papers) and Speech and Audio Processing (7 papers). Paul Lamere is often cited by papers focused on Music and Audio Processing (21 papers), Music Technology and Sound Studies (16 papers) and Speech and Audio Processing (7 papers). Paul Lamere collaborates with scholars based in United States, Canada and United Kingdom. Paul Lamere's co-authors include Thierry Bertin-Mahieux, Brian Whitman, Daniel P. W. Ellis, Douglas Eck, Rita Singh, Bhiksha Raj, Peter Wolf, Philip Kwok, Evandro Gouvêa and Willie Walker and has published in prestigious journals such as AI Magazine, ACM Transactions on Intelligent Systems and Technology and EURASIP Journal on Advances in Signal Processing.

In The Last Decade

Paul Lamere

27 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The Million Song Dataset

2011 445 citations Thierry Bertin-Mahieux, Daniel P. W. Ellis et al. Columbia Academic Commons (Columbia University) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Paul Lamere United States	14	890	716	636	302	185	28	1.5k
Douglas Turnbull United States	19	1.2k 1.3×	992 1.4×	502 0.8×	237 0.8×	322 1.7×	36	1.7k
Peter Knees Austria	21	943 1.1×	856 1.2×	339 0.5×	204 0.7×	191 1.0×	92	1.2k
Takuichi Nishimura Japan	14	724 0.8×	607 0.8×	339 0.5×	221 0.7×	133 0.7×	84	1.4k
Brian Whitman United States	11	879 1.0×	726 1.0×	353 0.6×	149 0.5×	141 0.8×	23	1.2k
Pedro Cano Spain	20	1.1k 1.2×	920 1.3×	280 0.4×	142 0.5×	144 0.8×	64	1.5k
Perfecto Herrera Spain	26	2.2k 2.5×	1.8k 2.5×	494 0.8×	270 0.9×	725 3.9×	142	2.8k
Roger B. Dannenberg United States	25	1.6k 1.8×	1.6k 2.3×	394 0.6×	158 0.5×	567 3.1×	165	2.4k
Bryan Pardo United States	25	1.5k 1.7×	943 1.3×	405 0.6×	102 0.3×	283 1.5×	127	1.9k
Man-Kwan Shan Taiwan	19	409 0.5×	386 0.5×	305 0.5×	366 1.2×	76 0.4×	60	1.0k
Arbee L. P. Chen Taiwan	19	516 0.6×	254 0.4×	560 0.9×	392 1.3×	47 0.3×	117	1.3k

Countries citing papers authored by Paul Lamere

Since Specialization

Citations

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

Fields of papers citing papers by Paul Lamere

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Lamere

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Ponnada, Aditya, et al.. (2023). Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems. TU/e Research Portal. 142–155. 12 indexed citations

2.

Chen, Ching-Wei, Paul Lamere, Markus Schedl, & Hamed Zamani. (2018). Recsys challenge 2018. 527–528. 61 indexed citations

3.

Lamere, Paul. (2012). I've got 10 million songs in my pocket. 207–208. 7 indexed citations

4.

Bertin-Mahieux, Thierry, Daniel P. W. Ellis, Brian Whitman, & Paul Lamere. (2011). The Million Song Dataset. Columbia Academic Commons (Columbia University). 591–596. 445 indexed citations breakdown →

5.

Celma, Òscar & Paul Lamere. (2011). Music recommendation and discovery revisited. 7–8. 13 indexed citations

6.

Celma, Òscar & Paul Lamere. (2011). If You Like Radiohead, You Might Like This Article. AI Magazine. 32(3). 57–66. 12 indexed citations

7.

Carman, Mark, et al.. (2010). Piloted search and recommendation with social tag cloud-based navigation. Solent University Research Portal (Solent University).

8.

Jehan, Tristan, Paul Lamere, & Brian Whitman. (2010). Music retrieval from everything. 245–246. 3 indexed citations

9.

Celma, Òscar, et al.. (2010). Workshop report. 381–382. 2 indexed citations

10.

Eck, Douglas, et al.. (2009). Steerable Playlist Generation By Learning Song Similarity From Radio Station Playlists.. Zenodo (CERN European Organization for Nuclear Research). 345–350. 36 indexed citations

11.

Lamere, Paul & Elias Pampalk. (2008). Social Tags and Music Information Retrieval.. International Symposium/Conference on Music Information Retrieval. 24. 9 indexed citations

12.

Bertin-Mahieux, Thierry, et al.. (2008). Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases. Journal of New Music Research. 37(2). 115–135. 73 indexed citations

13.

Eck, Douglas, Paul Lamere, Thierry Bertin-Mahieux, & Stephen Green. (2007). Automatic Generation of Social Tags for Music Recommendation. neural information processing systems. 20. 385–392. 121 indexed citations

14.

Lamere, Paul & Douglas Eck. (2007). Using 3D Visualizations To Explore And Discover Music.. Zenodo (CERN European Organization for Nuclear Research). 173–174. 30 indexed citations

15.

Eck, Douglas, Thierry Bertin-Mahieux, & Paul Lamere. (2007). Autotagging Music Using Supervised Machine Learning.. Zenodo (CERN European Organization for Nuclear Research). 367–368. 16 indexed citations

16.

West, Kris & Paul Lamere. (2006). A Model-Based Approach to Constructing Music Similarity Functions. EURASIP Journal on Advances in Signal Processing. 2007(1). 24 indexed citations

17.

West, Kris, Stephen Cox, & Paul Lamere. (2006). Incorporating machine-learning into music similarity estimation. UEA Digital Repository (University of East Anglia). 8. 89–96. 9 indexed citations

18.

Walker, Willie, Paul Lamere, Philip Kwok, et al.. (2004). Sphinx-4: a flexible open source framework for speech recognition. 279 indexed citations

19.

Singh, Rita, Manfred K. Warmuth, Bhiksha Raj, & Paul Lamere. (2003). Classification with free energy at raised temperatures. 1773–1776. 5 indexed citations

20.

Walker, Willie, et al.. (2002). FreeTTS: a performance case study. 17 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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