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.
librosa: Audio and Music Signal Analysis in Python
20151.7k citationsBrian McFee, Colin Raffel et al.Proceedings of the Python in Science Conferencesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Eric Battenberg
Since
Specialization
Citations
This map shows the geographic impact of Eric Battenberg'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 Battenberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Battenberg more than expected).
This network shows the impact of papers produced by Eric Battenberg. 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 Battenberg. The network helps show where Eric Battenberg may publish in the future.
Co-authorship network of co-authors of Eric Battenberg
This figure shows the co-authorship network connecting the top 25 collaborators of Eric Battenberg.
A scholar is included among the top collaborators of Eric Battenberg 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 Battenberg. Eric Battenberg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
15 of 15 papers shown
1.
Stanton, Daisy, Matt Shannon, Soroosh Mariooryad, et al.. (2022). Speaker Generation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 7897–7901.11 indexed citations
2.
Wang, Yuxuan, Daisy Stanton, Yu Zhang, et al.. (2018). Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis. International Conference on Machine Learning. 5180–5189.62 indexed citations
3.
McFee, Brian, Colin Raffel, Dawen Liang, et al.. (2015). librosa: Audio and Music Signal Analysis in Python. Proceedings of the Python in Science Conferences. 18–24.1676 indexed citations breakdown →
4.
McFee, Brian, Matt McVicar, Colin Raffel, et al.. (2015). librosa: 0.4.1. Zenodo (CERN European Organization for Nuclear Research).7 indexed citations
Battenberg, Eric, Victor Huang, & David Wessel. (2012). Toward Live Drum Separation Using Probabilistic Spectral Clustering Based on the Itakura-Saito Divergence.11 indexed citations
9.
Morgan, Nelson, David Wessel, & Eric Battenberg. (2012). Techniques for machine understanding of live drum performances.2 indexed citations
10.
Battenberg, Eric, Rimas Avižienis, Nils Peters, et al.. (2011). Real-time Musical Applications on an Experimental Operating System for Multi-Core Processors. The Journal of the Abraham Lincoln Association. 2011.5 indexed citations
11.
Battenberg, Eric, et al.. (2011). IMPLEMENTING REAL-TIME PARTITIONED CONVOLUTION ALGORITHMS ON CONVENTIONAL OPERATING SYSTEMS.14 indexed citations
12.
Battenberg, Eric, Adrian Freed, & David Wessel. (2010). Advances In The Parallelization Of Music And Audio Applications.. The Journal of the Abraham Lincoln Association. 2010.12 indexed citations
Battenberg, Eric & Mark Murphy. (2008). Parallelizing Audio Feature Extraction Using an Automatically Partitioned Streaming Dataow Language..1 indexed citations
15.
Battenberg, Eric, et al.. (2006). A System for Automatic Cell Segmentation of Bacterial Microscopy Images.2 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.