Audio source separation models are typically evaluated using objective separation quality measures, but rigorous statistical methods have yet to be applied to the problem of model comparison. As a result, it can be difficult to establish whether or not reliable progress is being made during the development of new models. In this paper, we provide a hypothesis-driven statistical analysis of the results of the recent source separation SiSEC challenge involving twelve competing models tested on separation of voice and accompaniment from fifty pieces of ‚Äúprofessionally produced‚ÄĚ contemporary music. Using nonparametric statistics, we establish reliable evidence for meaningful conclusions about the performance of the various models.


  author = {Simpson, A. J. R. and Roma, G. and Grais, Emad M. and Mason, R. D. and Hummersone, C. and Liutkus, A. and Plumbley, M. D.},
  title = {Evaluation of Audio Source Separation Models using Hypothesis-Driven Non-Parametric Statistical Methods},
  booktitle = {24th European Signal Processing Conference, {EUSIPCO} 2016, Budapest, Hungary, August 29 - September 2, 2016},
  pages = {1763--1767},
  year = {2016},
  openaccess = {http://epubs.surrey.ac.uk/811172/},
  doi = {10.1109/EUSIPCO.2016.7760551},
  keywords = {"maruss"}