Neural models for auditory localization based on spectral cues

In this paper we analyze several auditory localization neural models that are based on head related transfer functions (HRTFs). HRTFs represent the combined directional-spectral response of the pinnae head and torso. The role of HRTFs in such modeling has hitherto been underestimated despite substantial experimental evidence to its relevance in spatial hearing, especially in determining direction of high-frequency sound sources. In the first section we suggest a neural model that links the physiology of binaural processing to a neural network that extracts spectral ratios. These ratios correspond to HRTFs ratios and can provide auditory directional cues. Next, we compare several methods of matching HRTFs ratios using discriminative matching measure (DMM). We consider several solutions to the matching problem from a neural signal processing viewpoint. We compare correlation based approaches with DMM optimization approach and with a non-linear approach based on neural back-propagation algorithm. All three models can be implemented by neural networks. Finally, we include experimental results of simulations that are conducted using these methods. Experiments show that the back-propagation based neural network yields the best results in terms of DMM both for narrow-band and broad band excitation. The back-propagation neural network is also superior in matching noisy HRTF ratio vectors.