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In this paper, a new method for time–frequency analysis of speech signals is proposed. Given that the fundamental frequency of voiced speech often undergoes rapid fluctuation and in these cases the classical spectrogram suffers from blurring and artifacts, an adaptive analysis basis composed of quadratic chirps is what we consider. The analysis basis of the(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)
In radiotherapy (RT), organ motion caused by breathing prevents accurate patient positioning, radiation dose, and target volume determination. Most of the motion-compensated trial techniques require collaboration of the patient and expensive equipment. Estimating the motion between two computed tomography (CT) three-dimensional scans at the extremes of the(More)
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)
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)
This letter presents the exact normalized least-mean-square (NLMS) algorithm for the l<sub>p</sub>-norm-regularized square error, a popular choice for the identification of sparse systems corrupted by additive noise. The resulting exact lp-NLMS algorithm manifests differences to the original one, such as an independent update for each weight, a new(More)
  • Luis Weruaga
  • 2012
This paper presents the foundations of a novel method for chirplet signal decomposition. In contrast to basis-pursuit techniques on over-complete dictionaries, the proposed method uses a reduced set of adaptive parametric chirplets. The estimation criterion corresponds to the maximization of the likelihood of the chirplet parameters from redundant(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 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)
—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)