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ZENODO
Software . 2015
Data sources: Datacite
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ZENODO
Software . 2015
Data sources: ZENODO
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ABXpy v0.1

Authors: Thomas Schatz; Roland Thiolliere; Emmanuel Dupoux; Gabriel Synnaeve; Ewan Dunbar;
Abstract

{"references": ["T. Schatz, V. Peddinti, F. Bach, A. Jansen, H. Hermansky, and E. Dupoux, \u201cEvaluating speech features with the Minimal-Pair ABX task (I): Analysis of the classical MFC/PLP pipeline,\u201d in proceedings of INTERSPEECH, 2013.", "T. Schatz, V. Peddinti, X.-N. Cao, F. Bach, H. Hermansky, and E. Dupoux, \u201cEvaluating speech features with the Minimal-Pair ABX task (II): Resistance to noise,\u201d in proceedings of INTERSPEECH, 2014."]}

Python library for computing ABX discriminability scores over large datasets.

Keywords

ABX discriminability

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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