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.

Bibtex 


@inproceedings{Ward_2018b,
  title = {{SiSEC 2018: State of The Art in Musical Audio Source Separation - Subjective Selection of The Best Algorithm}},
  author = {Ward, D. and Mason, R. D. and Kim, C. and St{\"o}ter, F. R. and Liutkus, A. and Plumbley, M. D.},
  booktitle = {{WIMP: Workshop on Intelligent Music Production}},
  address = {Huddersfield, United Kingdom},
  year = {2018},
  month = sep,
  keywords = {"maruss"},
  openaccess = {http://epubs.surrey.ac.uk/849086/}
}