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ICONIP
2008

A Novel Approach for Hardware Based Sound Classification

10 years 1 months ago
A Novel Approach for Hardware Based Sound Classification
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 system solution in hardware. The system is based on the signal processing modules of a previously proposed sound processing system, which convert the input signal in spike trains. The feature generation method creates simple binary features vectors, used as the training data of a standard LVQ neural network. An output temporal layer uses the time information of the sound signals in order to eliminate the misclassifications of the classifier. The result is a robust, hardware friendly model for sound classification, presenting high accuracy for the eight sound source signals used on the experiments, while requiring small FPGA logic and...
Mauricio Kugler, Victor Alberto Parcianello Benso,
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ICONIP
Authors Mauricio Kugler, Victor Alberto Parcianello Benso, Susumu Kuroyanagi, Akira Iwata
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