Non-negative Matrix Factor Deconvolution
Implements both Kullback-Leibler divergence method from Smaragdis and Least Squares method from Schmidt and Morup and Wang, Cichocki, and Chambers.
Schmidt, M. N. & Morup, M. (2006). Non-negative matrix factor 2-d deconvolution for blind single channel source separation. Independent Component Analysis, International Conference on (ICA), Springer Lecture Notes in Computer Science, Vol.3889, 700-707. Retrieved from http://mikkelschmidt.dk/papers/schmidt2006ica.pdf
Smaragdis, P. (2004). Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 494-499. Retrieved from http://www.merl.com/reports/docs/TR2004-104.pdf
Wang, W., Cichocki, A., & Chambers. J. (2009). A multiplicative algorithm for convolutive non-negative matrix factorization based on squared euclidean distance. IEEE Transactions on Signal Processing, 57, (7), 2858-2864.