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 memory resources.
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