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» Toward Learning Gaussian Mixtures with Arbitrary Separation
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ICASSP
2011
IEEE
12 years 11 months ago
An acoustically-motivated spatial prior for under-determined reverberant source separation
We consider the task of under-determined reverberant audio source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a ze...
Ngoc Q. K. Duong, Emmanuel Vincent, Rémi Gr...
UAI
2000
13 years 9 months ago
Gaussian Process Networks
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
Nir Friedman, Iftach Nachman
ICDM
2008
IEEE
193views Data Mining» more  ICDM 2008»
14 years 2 months ago
Multiplicative Mixture Models for Overlapping Clustering
The problem of overlapping clustering, where a point is allowed to belong to multiple clusters, is becoming increasingly important in a variety of applications. In this paper, we ...
Qiang Fu, Arindam Banerjee
PAMI
2008
161views more  PAMI 2008»
13 years 7 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...
ICASSP
2011
IEEE
12 years 11 months ago
A partial least squares framework for speaker recognition
Modern approaches to speaker recognition (verification) operate in a space of “supervectors” created via concatenation of the mean vectors of a Gaussian mixture model (GMM) a...
Balaji Vasan Srinivasan, Dmitry N. Zotkin, Ramani ...