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SISEC 2018: state of the art in musical audio source separation - Subjective selection of the best algorithm

Ward, Dominic, Mason, Russell D., Kim, Ryan Chungeun, Stöter, Fabian-Robert, Liutkus, Antoine and Plumbley, Mark D. (2018) SISEC 2018: state of the art in musical audio source separation - Subjective selection of the best algorithm In: 4th Workshop on Intelligent Music Production, WIMP 2018, 14 Sep 2018, University of Huddersfield, Huddersfield, UK.

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The Signal Separation Evaluation Campaign (SiSEC) is a large-scale regular event aimed at evaluating current progress in source separation through a systematic and reproducible comparison of the participants’ algorithms, providing the source separation community with an invaluable glimpse of recent achievements and open challenges. This paper focuses on the music separation task from SiSEC 2018, which compares algorithms aimed at recovering instrument stems from a stereo mix. In this context, we conducted a subjective evaluation whereby 34 listeners picked which of six competing algorithms, with high objective performance scores, best separated the singing-voice stem from 13 professionally mixed songs. The subjective results reveal strong differences between the algorithms, and highlight the presence of song-dependent performance for state-of-the-art systems. Correlations between the subjective results and the scores of two popular performance metrics are also presented.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Mason, Russell
Kim, Ryan
Stöter, Fabian-Robert
Liutkus, Antoine
Plumbley, Mark
Date : 14 September 2018
Copyright Disclaimer : Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Related URLs :
Depositing User : Clive Harris
Date Deposited : 24 Aug 2018 00:09
Last Modified : 14 Sep 2018 02:08

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