Post-Cochlear Auditory Modelling for Sound Localisation using Bio-Inspired Techniques

This thesis presents spiking neural architectures which simulate the sound localisation capability of the mammalian auditory pathways. This localisation ability is achieved by exploiting important differences in the sound stimulus received by each ear, known as binaural cues. Interaural time difference and interaural intensity difference are the two binaural cues which play the most significant role in mammalian sound localisation. These cues are processed by different regions within the auditory pathways and enable the localisation of sounds at different frequency ranges; interaural time difference is used to localise low frequency sounds whereas interaural intensity difference localises high frequency sounds. Interaural time difference refers to the different points in time at which a sound from a single location arrives at each ear and interaural intensity difference refers to the difference in sound pressure levels of the sound at each ear, measured in decibels. Taking inspiration from the mammalian brain, two spiking neural network topologies were designed to extract each of these cues. The architecture of the spiking neural network designed to process the interaural time difference cue was inspired by the medial superior olive. The lateral superior olive was the inspiration for the architecture designed to process the interaural intensity difference cue. The development of these spiking neural network architectures required the integration of other biological models, such as an auditory periphery (cochlea) model, models of bushy cells and the medial nucleus of the trapezoid body, leaky integrate and fire spiking neurons, facilitating synapses, receptive fields and the appropriate use of excitatory and inhibitory neurons. Two biologically inspired learning algorithms were used to train the architectures to perform sound localisation. Experimentally derived HRTF acoustical data from adult domestic cats was employed to validate the localisation ability of the two architectures. The localisation abilities of the two models are comparable to other computational techniques employed in the literature. The experimental results demonstrate that the two SNN models behave in a similar way to the mammalian auditory system, i.e. the spiking neural network for interaural time difference extraction performs best when it is localising low frequency data, and the interaural intensity difference spiking neuron model performs best when it is localising high frequency data. Thus, the combined models form a duplex system of sound localisation. Additionally, both spiking neural network architectures show a high degree of robustness when the HRTF acoustical data is corrupted by noise.