TY - GEN
T1 - Bayesian inference for nonnegative matrix factor deconvolution models
AU - Kirbiz, Serap
AU - Cemgil, Ali Taylan
AU - Günsel, Bilge
PY - 2010
Y1 - 2010
N2 - In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extraction of multiple sound objects from a single channel auditory scene. The proposed method facilitates automatic model selection and determination of the sparsity criteria. Our approach retains attractive features of standard NMFD based methods such as fast convergence and easy implementation. We demonstrate the use of this algorithm in the log-frequency magnitude spectrum domain, where we employ it to perform model order selection and control sparseness directly.
AB - In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extraction of multiple sound objects from a single channel auditory scene. The proposed method facilitates automatic model selection and determination of the sparsity criteria. Our approach retains attractive features of standard NMFD based methods such as fast convergence and easy implementation. We demonstrate the use of this algorithm in the log-frequency magnitude spectrum domain, where we employ it to perform model order selection and control sparseness directly.
UR - http://www.scopus.com/inward/record.url?scp=78149486872&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2010.689
DO - 10.1109/ICPR.2010.689
M3 - Conference contribution
AN - SCOPUS:78149486872
SN - 9780769541099
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2812
EP - 2815
BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
T2 - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Y2 - 23 August 2010 through 26 August 2010
ER -