Mauricio Kugler

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SUMMARY This paper presents a Complex-Valued Neural Network-based sound localization method. The proposed approach uses two microphones to localize sound sources in the whole horizontal plane. The method uses time delay and amplitude difference to generate a set of features which are then classified by a Complex-Valued Multi-Layer Percep-tron. The advantage(More)
Modern applications of pattern recognition generate very large amounts of data, which require large computational effort to process. However, the majority of the methods intended for large-scale problems aim to merely adapt standard classification methods without considering if those algorithms are appropriated for large-scale problems. CombNET-II was one(More)
This work reports the use of neural networks for pattern recognition in electroencephalographic signals related to intermittent photic-stimulation. Due to the low signal/noise ratio of this kind of signal, it was necessary the use of a spectrogram as a predictor and a chain of LVQ neural networks. The efficiency of this pattern recognition structure was(More)
A prosthetic keyboard for a Brain Computer Interface (BCI) is a device that uses patterns of the electroencephalographic (EEG) signal evoked by specific visual stimuli to recognize symbols or commands. When the user looks to a stimulus, an associated pattern on the EEG signal will be evoked. The objective of this project is to develop a system for signal(More)
Several applications would emerge from the development of efficient and robust sound classification systems able to identify the nature of non-speech sound sources. This paper proposes a novel approach that combines a simple feature generation procedure, a supervised learning process and fewer parameters in order to obtain an efficient sound classification(More)
The Support Vector Machines (SVMs) had been showing a high capability of complex hyperplane representation and great generalization power. These characteristics lead to the development of more compact and less computational complex methods than the One-versus-Rest (OvR) and One-versus-One (OvO) [1] classical methods in the application of SVMs in multiclass(More)
Several research fields have to deal with very large classification problems, e.g. handwritten character recognition and speech recognition. Many works have proposed methods to address problems with large number of samples, but few works have been done concerning problems with large numbers of classes. CombNET-II was one of the first methods proposed for(More)
Several applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. This paper proposes an integrated sound localization and classification system based on the human auditory system and a respective compact hardware implementation. The proposed models are based on spiking neurons, which are(More)