Adaptive environment classification system for hearing aids

An adaptive sound classification framework is proposed for hearing aid applications. The long-term goal is to develop fully trainable instruments in which both the acoustical environments encountered in daily life and the hearing aid settings preferred by the user in each environmental class could be learned. Two adaptive classifiers are described, one based on minimum distance clustering and one on Bayesian classification. Through unsupervised learning, the adaptive systems allow classes to split or merge based on changes in the ongoing acoustical environments. Performance was evaluated using real-world sounds from a wide range of acoustical environments. The systems were first initialized using two classes, speech and noise, followed by a testing period when a third class, music, was introduced. Both systems were successful in detecting the presence of an additional class and estimating its underlying parameters, reaching a testing accuracy close to the target rates obtained from best-case scenarios derived from non-adaptive supervised versions of the classifiers (about 3% lower performance). The adaptive Bayesian classifier resulted in a 4% higher overall accuracy upon splitting adaptation than the minimum distance classifier. Merging accuracy was found to be the same in the two systems and within 1%-2% of the best-case supervised versions.     

Robust Tracking of a Lightweight Manipulator System

A robust control design for high performance joint trajectory tracking of a flexible lightweight manipulator system is proposed. The design is based on a combined controller-observer scheme involving the sliding manifold approach and the optimal interpolation technique. This controller provides the designer with an enhanced joint tracking performance when the system is subject to parametric variations due to structural disturbances caused by link flexibility and load uncertainties. The parametric variations are handled by sliding control and the estimation of the nonlinearly excited elastic dynamics by an optimal interpolator of the structure's dynamic responses. The design procedure is progressive, i.e., we start with a basic controller and then modify it in order to improve the performance. Closed loop simulations with the various designed controllers are used to validate the analytical results and to help choosing the most suitable one.