This paper proposes an exemplar-based speech enhancement method based on high-resolution STFT magnitude spectrograms, where a selection of the nonnegative training data is used as the dictionary to provide a holistic nonnegative representation of the test data. We discuss how this exemplar-based model ensures that the enhanced speech signal falls on the speech manifold, which improves the quali...

This paper presents a voice conversion (VC) technique for noisy environments based on a sparse representation of speech. In our previous work, we discussed an exemplar-based VC technique for noisy environments. In that report, source exemplars and target exemplars are extracted from the parallel training data, having the same texts uttered by the source and target speakers. The input source sig...

This paper presents a voice conversion (VC) technique for noisy environments based on a sparse representation of speech. Sparse representation-based VC using Non-negative matrix factorization (NMF) is employed for noise-added spectral conversion between different speakers. In our previous exemplar-based VC method, source exemplars and target exemplars are extracted from parallel training data, ...

In the present study, GECLE (Matsuka, 2003) was used as a general modeling framework to systematically compare the plausibility of two prominent assumptions about internal representations of neural network (NN) models of human category learning. In particular, exemplar-model friendly Medin and Schaffer’s 5/4 stimulus set (1978) was used for comparing prototypeand exemplar-based NN models. The r...

Most studies of multiple-cue judgment focus on learning by individuals. In a multiple-cue judgment task we examined if people acquire rule or exemplar knowledge as a function of learning the task alone or in dyads. The expectation was that learning in dyads should promote explicit rule-based thinking as a consequence of increased verbalization (a social abstraction effect) and produce a larger ...

Exemplar-based clustering methods have been shown to produce state-of-the-art results on a number of synthetic and real-world clustering problems. They are appealing because they offer computational benefits over latent-mean models and can handle arbitrary pairwise similarity measures between data points. However, when trying to recover underlying structure in clustering problems, tailored simi...

Recovering lost part of an image plays a great role in image processing. Inpainting is a technique that helps in recovering lost pixels from an image. From the existing techniques of Inpainting, Exemplar Inpainting is one of the fast and better techniques that help in restoring the lost part of an image. Exemplar based method chooses a patch similar to the lost patch from the known area to fill...

Rotationsinvarianz in exemplarbasierter Bildvervollständigung Autor: Martin Eller Über exemplarbasierte Bildvervollständigungsmethoden wurde, wegen ihrer Leistungsstärke in der Anwendung auf Bildern mit Textur, in den letzten Jahren viel geforscht. In dieser Thesis wird das variationelle Modell für exemplarbasierte Bildvervollständigung um einen zusätzlichen Freiheitsgrad, die Rotationsinvarian...

A central problem in case based reasoning (CBR) is how to store and retrieve cases. One approach to this problem is to use exemplar based models, where only the prototypical cases are stored. However, the development of an ex-emplar based model (EBM) requires the solution of several problems: (i) how can a EBM be represented? (ii) given a new case, how can a suitable exemplar be retrieved? (iii...

A nonlocal variational formulation for interpolating a sparsely sampled image is introduced in this paper. The proposed variational formulation, originally motivated by image inpainting problems, encourages the transfer of information between similar image patches, following the paradigm of exemplar-based methods. Contrary to the classical inpainting problem, no complete patches are available f...