Skip to Main content Skip to Navigation
Conference papers

SiSEC 2018: State of the art in musical audio source separation - subjective selection of the best algorithm

Dominic Ward 1 Russel D. Mason 2 Chungeun Kim 1 Fabian-Robert Stöter 3 Antoine Liutkus 3 Mark Plumbley 1
3 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : 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.
Document type :
Conference papers
Complete list of metadatas

https://hal.inria.fr/hal-01945362
Contributor : Antoine Liutkus <>
Submitted on : Wednesday, December 5, 2018 - 11:45:14 AM
Last modification on : Monday, May 4, 2020 - 10:12:02 AM
Long-term archiving on: : Wednesday, March 6, 2019 - 2:08:06 PM

File

SiSEC%202018.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

  • HAL Id : hal-01945362, version 1

Collections

Citation

Dominic Ward, Russel D. Mason, Chungeun Kim, Fabian-Robert Stöter, Antoine Liutkus, et al.. SiSEC 2018: State of the art in musical audio source separation - subjective selection of the best algorithm. WIMP: Workshop on Intelligent Music Production, Sep 2018, Huddersfield, United Kingdom. ⟨hal-01945362⟩

Share

Metrics

Record views

472

Files downloads

461