A novel approach for hardware based sound localization

Abstract

Sound localization is an important ability intrinsic to animals, being currently explored by several researches. Even though several systems and implementations have being proposed, the majority is very complex and not suitable for embedded systems. This paper proposes a new approach for binaural sound localization and the corresponding implementation in an Field Programable Gate Array (FPGA) device. The system is based on the signal processing modules of a previously proposed sound processing system, which converts the input signal to spike trains. The time difference extraction and feature generation methods introduced in this paper create simple binary feature vectors, used as training data for a standard LVQ neural network. An output temporal layer uses the time information of the sound signals in order to reduce the misclassifications of the classifier. Preliminary experimental results show high accuracy with small logic and memory requirements. © 2010 Springer-Verlag Berlin Heidelberg.