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Every speaker GMM is adapted from a background model using the eigenvoice approach[23].
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In essence, we expect to see few outlying over-representation scores for TF binding models, one of which should be for the ChIP'd TF's binding model, with the majority of binding models scoring close to 0. We summarize the results for each of the alternative background models using these measures in Figure 2 and Additional file 5: Figure S4.
Several algorithms have been developed to estimate the "background model" using temporal information [37].
It first fits a background model using genomic DNA and then feeds the estimated parameters into a Bayesian model that combines information from multiple SNPs within a gene to infer ASE.
Next, the background modelling module is employed in order to generate a high-quality adaptive background model using a unique two-stage training procedure and a mechanism for recognizing changes in illumination.
Sinusoidal models are also employed to model speakers for joint speaker separation and identification [20], and SNR estimation can be achieved by adapting a universal background model using segregated speech [21].
In such a case, removing such repeats will improve the estimation of the background model used in the phylogenetic footprinting.
The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model.
The background model used is based on Elgammal and Duraiswami's work [33] that use kernel density estimation to model the background.
The background model used in this work models each pixel as a mixture of Gaussians with an on-line approximation used for updating the model.
It is free from any assumption on the model dependency, and it escapes from the use of background modeling using Markov chain models.
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