Evaluation of Audio Source Separation Models Using Hypothesis-Driven Non-Parametric Statistical Methods - Archive ouverte HAL Access content directly
Conference Papers Year :

Evaluation of Audio Source Separation Models Using Hypothesis-Driven Non-Parametric Statistical Methods

(1) , (1) , (1) , (1) , (1) , (2) , (1)
1
2

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.
Fichier principal
Vignette du fichier
Separation_Stats.pdf (249.85 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01410176 , version 1 (06-12-2016)

Identifiers

  • HAL Id : hal-01410176 , version 1

Cite

Andrew J R Simpson, Gerard Roma, Emad M Grais, Russell D Mason, Chris Hummersone, et al.. Evaluation of Audio Source Separation Models Using Hypothesis-Driven Non-Parametric Statistical Methods. European Signal Processing Conference, EURASIP, Aug 2016, Budapest, Hungary. ⟨hal-01410176⟩
148 View
179 Download

Share

Gmail Facebook Twitter LinkedIn More