Julian L. Cardenas-Barrera

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Coding distortion in lossy electroencephalographic (EEG) signal compression methods is evaluated through tractable objective criteria. The percentage root-mean-square difference, which is a global and relative indicator of the quality held by reconstructed waveforms, is the most widely used criterion. However, this parameter does not ensure compliance with(More)
This paper studies the effect of feature dimensions on an MFCC/IMFCC-GMM based text-independent speaker verification system (SVS). A typical baseline system is used to evaluate the impact of features based on the number of Mel, inverted Mel, delta and double delta coefficients while keeping other system parameters constant for all experiments. The relevance(More)
This paper presents a novel neural network-based approach to short-term, multi-step-ahead wind speed forecasting. The methodology combines predictions from a set of feed forward neural networks whose inputs comprehend a set of 11 explanatory variables related to past averages of wind speed, direction, temperature and time of the day, and their outputs(More)
The recent use of long-term records in electroencephalography is becoming more frequent due to its diagnostic potential and the growth of novel signal processing methods that deal with these types of recordings. In these cases, the considerable volume of data to be managed makes compression necessary to reduce the bit rate for transmission and storage(More)
This paper describes an adaptive solution for threshold management of speaker verification systems. The reported algorithm estimates speaker-dependent thresholds based on successful verification sessions considering the minimization of a relation-based cost function. The optimum speaker verification threshold to authenticate a remotely located client is not(More)
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