Neuromorphic model for sound source segregation

While humans can easily segregate and track a speaker's voice in a loud noisy environment, most modern speech recognition systems still perform poorly in loud background noise. The computational principles behind auditory source segregation in humans is not yet fully understood. In this dissertation, we develop a computational model for source segregation inspired by auditory processing in the brain. To support the key principles behind the computational model, we conduct a series of electro-encephalography experiments using both simple tone-based stimuli and more natural speech stimulus. Most source segregation algorithms utilize some form of prior information about the target speaker or use more than one simultaneous recording of the noisy speech mixtures. Other methods develop models on the noise characteristics. Source segregation of simultaneous speech mixtures with a single microphone recording and no knowledge of the target speaker is still a challenge. Using the principle of temporal coherence, we develop a novel computational model that exploits the difference in the temporal evolution of features that belong to different sources to perform unsupervised monaural source segregation. While using no prior information about the target speaker, this method can gracefully incorporate knowledge about the target speaker to further enhance the segregation.Through a series of EEG experiments we collect neurological evidence to support the principle behind the model. Aside from its unusual structure and computational innovations, the proposed model provides testable hypotheses of the physiological mechanisms of the remarkable perceptual ability of humans to segregate acoustic sources, and of its psychophysical manifestations in navigating complex sensory environments. Results from EEG experiments provide further insights into the assumptions behind the model and provide motivation for future single unit studies that can provide more direct evidence for the principle of temporal coherence.

Leader Based Cyclic Pursuit

In this work a system of autonomous agents engaged in cyclic pursuit (under constant bearing (CB) strategy) is considered, for which one informed agent (the leader) also senses and responds to a stationary beacon. Building on the framework proposed in a previous work on beacon-referenced cyclic pursuit, necessary and suffi- cient conditions for the existence of circling equilibria in a system with one informed agent are derived, with discussion of stability and performance. In a physical testbed, the leader (robot) is equipped with a sound sensing apparatus composed of a real time embedded system, estimating direction of arrival of sound by an Interaural Level and Phase Difference Algorithm, using empirically determined phase and level signatures, and breaking front-back ambiguity with appropriate sensor placement. Furthermore a simple framework for implementing and evaluating the performance of control laws with the Robot Operating System (ROS) is proposed, demonstrated, and discussed.