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

Autor: Ward, Dominic, Mason, Russell D., Kim, Ryan Chungeun, Stöter, Fabian-Robert, Liutkus, Antoine, Mark Plumbley
Přispěvatelé: Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey (UNIS), Institute of Sound Recording (IoSR), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-15-CE38-0003,KAMoulox,Démixage en ligne de larges archives sonores(2015), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: 4th Workshop on Intelligent Music Production
WIMP: Workshop on Intelligent Music Production
WIMP: Workshop on Intelligent Music Production, Sep 2018, Huddersfield, United Kingdom
ResearcherID
Popis: International audience; 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.
Databáze: OpenAIRE
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