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This paper presents a novel transform related to the framework of warping operators when the continuous time warping mapping is a second-order polynomial. This case is proven in the paper to be the only one from the aforementioned group that marginalizes the Wigner distribution along line paths, in particular, with a fan geometry. The properties and(More)
Active deformable models are simple tools, very popular in computer vision and computer graphics, for solving ill-posed problems or mimic real physical systems. The classical formulation is given in the spatial domain, the motor of the procedure is a second-order linear system, and rigidity and elasticity are the basic parameters for its characterization.(More)
The Chirp transform is a powerful analysis tool for variable frequency signals such as speech. The computational load represents the main limitation of its original formulation, discouraging its use in real-time applications. This paper analyzes a fast implementation, based on performing time-warping on the signal under analysis, combined with the Fast(More)
—This paper presents a novel method for noise-compensated autoregressive estimation founded on the maximum likelihood of the spectral samples. This framework yields a non-linear optimization problem that can be revamped as a re-weighted least square problem. The resulting spectral weighting function turns out to be the square of the Wiener filter, this(More)
This paper introduces a new spectral representation-based pitch estimation method. Since pitch is never stationary during real conversations, but often undergoes changes because of intonation, the spectral representation is derived from the Short-time Harmonic Chirp Transform. This lets our technique to perform very well in noisy conditions, and to extract(More)
Frequency-selective autoregressive (AR) estimation is arousing increasing interest. We propose herein a new method to estimate the AR model from a reduced set of spectral samples. The proposed method is founded on the maximum likelihood criterion over the logarithmic spectral residue, and it is implemented efficiently with a multivariate Newton-Raphson(More)
One research direction to make computers become more active machines is to use audio input with far-field omni-directional microphones, these being required to face the acoustics problem of the meeting scenario. This topic is not only interesting from an artificial listener point-of-view, but also as one of the hottest topics in searchable media. In this(More)
The architecture of the cerebellar model articulation controller (CMAC) presents a rigid compromise between learning and generalization. In the presence of a sparse training dataset, this limitation manifestly causes overfitting, a drawback that is not overcome by current training algorithms. This paper proposes a novel training framework founded on the(More)