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Psychophysical Evaluation of Audio Source Separation Methods

Simpson, AJR, Roma, G, Grais, EM, Mason, Russell, Hummersone, Christopher and Plumbley, Mark (2017) Psychophysical Evaluation of Audio Source Separation Methods In: 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), 2017-02-21 - 2017-02-23, Grenoble, France.

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Abstract

Source separation evaluation is typically a top-down process, starting with perceptual measures which capture fitness-for-purpose and followed by attempts to find physical (objective) measures that are predictive of the perceptual measures. In this paper, we take a contrasting bottom-up approach. We begin with the physical measures provided by the Blind Source Separation Evaluation Toolkit (BSS Eval) and we then look for corresponding perceptual correlates. This approach is known as psychophysics and has the distinct advantage of leading to interpretable, psychophysical models. We obtained perceptual similarity judgments from listeners in two experiments featuring vocal sources within musical mixtures. In the first experiment, listeners compared the overall quality of vocal signals estimated from musical mixtures using a range of competing source separation methods. In a loudness experiment, listeners compared the loudness balance of the competing musical accompaniment and vocal. Our preliminary results provide provisional validation of the psychophysical approach

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Simpson, AJRUNSPECIFIEDUNSPECIFIED
Roma, GUNSPECIFIEDUNSPECIFIED
Grais, EMUNSPECIFIEDUNSPECIFIED
Mason, RussellR.Mason@surrey.ac.ukUNSPECIFIED
Hummersone, Christopherc.hummersone@surrey.ac.ukUNSPECIFIED
Plumbley, Markm.plumbley@surrey.ac.ukUNSPECIFIED
Date : 15 February 2017
Identification Number : 10.1007/978-3-319-53547-0_21
Copyright Disclaimer : The final publication is available at link.springer.com
Contributors :
ContributionNameEmailORCID
UNSPECIFIEDSpringer, UNSPECIFIEDUNSPECIFIED
Uncontrolled Keywords : Deep learning, source separation, perceptual evaluation
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 14 Dec 2016 16:08
Last Modified : 23 Aug 2017 07:53
URI: http://epubs.surrey.ac.uk/id/eprint/813113

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