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The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms...
This paper investigates the use of Gaussian processes to detect non-linearly mixed pixels in hyperspectral images. The proposed technique is independent of nonlinear mixing mechanism, and therefore is not restricted to any prescribed nonlinear mixing model. The observed reflectances are estimated using both the least squares method and a Gaussian process. The fitting errors of the two approaches are...
This work describes GASpeech: a framework centered on genetic algorithms for automatically estimating the input parameters of Klatt's speech synthesizer. GASpeech aims to speed up the process of speech imitation (or utterance copy), where one has to find the model parameters that lead to a synthesized speech sounding close enough to the natural target speech (i.e. low spectral distortion). The architecture...
Speech processing is a data-driven technology that relies on public corpora and associated resources. In contrast to languages such as English, there are few resources for Brazilian Portuguese (BP). Consequently, there are no publicly available scripts to design baseline BP systems. This work discusses some efforts towards decreasing this gap and presents results for two speech processing tasks for...
This paper describes an open source framework for developing speaker recognition systems. Among other features, it supports kernel classifiers, such as the support and relevance vector machines. The paper also presents results for the IME corpus using Gaussian mixture models, which outperforms previously published ones, and discusses strategies for applying discriminative classifiers to speaker recognition
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