Evaluation of Audio Source Separation Models Using Hypothesis-Driven Non-Parametric Statistical MethodsReport as inadecuate




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1 Centre for Digital Music 2 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery

Abstract : Audio source separation models are typically evaluated using objective separation quality measures, but rigorous statistical methods have yet to be applied to the problem of model comparison. As a result, it can be difficult to establish whether or not reliable progress is being made during the development of new models. In this paper, we provide a hypothesis-driven statistical analysis of the results of the recent source separation SiSEC challenge involving twelve competing models tested on separation of voice and accompaniment from fifty pieces of - professionally produced - contemporary music. Using non-parametric statistics, we establish reliable evidence for meaningful conclusions about the performance of the various models.

Keywords : Audio source separation BSSeval SiSEC Hypothesis test





Author: Andrew Simpson - Gerard Roma - Emad Grais - Russell Mason - Chris Hummersone - Antoine Liutkus - Mark Plumbley -

Source: https://hal.archives-ouvertes.fr/



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