BSS Eval 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.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Kühne, Marco; Togneri, Roberto; Nordholm, Sven: A novel fuzzy clustering algorithm using observation weighting and context information for reverberant blind speech separation (2010)
- Litvin, Yevgeni; Cohen, Israel; Chazan, Dan: Monaural speech/music source separation using discrete energy separation algorithm (2010)
- O’Grady, Paul D.; Pearlmutter, Barak A.: The LOST algorithm: Finding lines and separating speech mixtures (2008)
- Jafari, Maria G.; Plumbley, Mark D.: The role of high frequencies in convolutive blind source separation of speech signals (2007)
- Mitianoudis, Nikolaos; Stathaki, Tania: Underdetermined source separation using mixtures of warped Laplacians (2007)
- O’Grady, Paul D.; Pearlmutter, Barak A.: Discovering convolutive speech phones using sparseness and non-negativity (2007)
- Hiroe, Atsuo: Solution of permutation problem in frequency domain ICA, using multivariate probability density functions (2006)
- Jafari, Maria G.; Abdallah, Samer A.; Plumbley, Mark D.; Davies, Mike E.: Sparse coding for convolutive blind audio source separation (2006)