Bioinspired Auditory Sound Localisation for Improving the Signal to Noise Ratio of Socially Interactive Robots

Abstract

In this paper we describe a bioinspired hybrid architecture for acoustic sound source localisation and tracking to increase the signal to noise ratio (SNR) between speaker and background sources for a socially interactive robot's speech recogniser system. The model presented incorporates the use of interaural time difference for azimuth estimation and recurrent neural networks for trajectory prediction. The results are then presented showing the difference in the SNR of a localised and non-localised speaker source, in addition to presenting the recognition rates between a localised and non-localised speaker source. From the results presented in this paper it can be seen that by orientating towards the sound source of interest the recognition rates of that source can be increased