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We consider the noiseless linear independent component analysis problem, in the case where the hidden sources s are nonnegative. We assume that the random variables si are well grounded in that they have a nonvanishing probability density function (PDF) in the (positive) neighborhood of zero. For an orthonormal rotation y=Wx of prewhitened observations(More)
In this paper we consider the problem of representing a speech signal with an adaptive transform that captures the main features of the data. The transform is orthogonal by construction, and is found to give a sparse representation of the data being analysed. The orthogonality property implies that evaluation of both the forward and inverse transform(More)
Harmonic sinusoidal models are a fundamental tool for audio signal analysis. Bayesian harmonic models guarantee a good resynthesis quality and allow joint use of learnt parameter priors and auditory motivated distortion measures. However inference algorithms based on Monte Carlo sampling are rather slow for realistic data. In this paper, we investigate fast(More)
This article deals with low bitrate object coding of musical audio, and more precisely with the extraction of pitched sound objects in polyphonic music. After a brief review of existing methods, we discuss the potential benefits of recasting this problem in a Bayesian framework. We define pitched objects by a set of probabilistic priors and derive efficient(More)
Summary form only given. The authors examine the goals of early stages of a perceptual system, before the signal reaches the cortex, and describe its operation in information-theoretic terms. The effects of receptor adaptation, lateral inhibition, and decorrelation can all be seen as part of an optimization of information throughput, given that available(More)
We describe a nonparametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning. The estimator uses an adaptive partitioning method and runs in Theta(<i>N</i> <sub>log</sub> <i>N</i>) time, with low memory requirements. In experiments using known(More)
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