A toolbox for performance measurement in (blind) source separation
BSS Eval is a MATLAB toolbox to measure the performance of (blind) source separation algorithms within an evaluation framework where the original source signals are available as ground truth [1, 3]. The measures are based on the decomposition of each estimated source signal into a number of contributions corresponding to the target source, interference from unwanted sources, and artifacts such as "musical noise". They are valid for any type of data (audio, biomedical, etc), any mixture (instantaneous, convolutive, etc) and any algorithm (beamforming, ICA, time-frequency masking, etc).
For audio data, the resulting energy ratio criteria correlate with subjective ratings to a certain extent only. For improved correlation with subjective ratings, try our latest toolkit PEASS.
- Version 3.0 for Matlab (18 kB) by E. Vincent
This version provides three complementary functions forming the core of the annual Signal Separation Evaluation Campaign (SiSEC) :
This version is recommended for mixtures of reverberated or diffuse sources (aka convolutive mixtures), due to longer decomposition filters enabling better correlation with subjective ratings. It also recommended for instantaneous mixtures when the results are to be compared with SiSEC.
- bss_eval_sources.m: for the evaluation of estimated single-channel source signals 
- bss_eval_images.m: for the evaluation of estimated multichannel spatial source images [3, 4]
- bss_eval_mix.m: for the evaluation of estimated mixing filters [3, 5]
- Version 2.1 for Matlab (272 kB) by C. Févotte, R. Gribonval and E. Vincent
This version was developed with the support of GDR ISIS. It provides detailed evaluation capabilities (distinction between additive noise and other interfering sources, time-varying performance metrics) [1, 2] but it is practically restricted to instantaneous mixtures of point sources. It is recommended for such mixtures, except when the results are to be compared with SiSEC.
For examples of use of Version 3, see example_inst.m and example_conv.m on the Signal Separation Evaluation Campaign website.
Examples of use of Version 2.1 taken from our paper  are available here.
- E. Vincent, R. Gribonval and C. Févotte, Performance measurement in blind audio source separation, IEEE Trans. Audio, Speech and Language Processing, 14(4), pp 1462-1469, 2006.
- C. Févotte, R. Gribonval and E. Vincent, BSS_EVAL toolbox user guide - Revision 2.0, Technical Report 1706, IRISA, April 2005.
- E. Vincent, S. Araki, F.J. Theis, G. Nolte, P. Bofill, H. Sawada, A. Ozerov, B.V. Gowreesunker, D. Lutter and N.Q.K. Duong, The Signal Separation Evaluation Campaign (2007-2010): Achievements and remaining challenges, Signal Processing, 92, pp. 1928-1936, 2012.
- E. Vincent, H. Sawada, P. Bofill, S. Makino and J.P. Rosca, First stereo audio source separation evaluation campaign: Data, algorithms and results, in Proc. 7th Int. Conf. on Independent Component Analysis and Signal Separation (ICA), pp 552-559, 2007.
- E. Vincent, S. Araki and P. Bofill, The 2008 Signal Separation Evaluation Campaign: A community-based approach to large-scale evaluation, in Proc. 8th Int. Conf. on Independent Component Analysis and Signal Separation (ICA), pp 734-741, 2009.