Factor graphs allow large probability distributions to be stored efficiently and facilitate fast computation of marginal probabilities, but the difficulty of training them has limited their use. Given a large set of data points, the training process should yield factors for which the observed data has a high likelihood. We present a factor graph learning algorithm which on each iteration merges...

The problem of undecided Separating reverberant audio sources is crucial for speech and processing. Numerous separation strategies have been developed to solve this problem; however, all them estimate model parameters in the time–frequency domain, resulting permutation ambiguity poor performance. Additionally, one main challenges with existing expectation–maximization (EM) time needed each iter...

Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up conve...