Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two timescale analysis of the Alopex algorithm for optimization
Neural Computation
Efficient greedy learning of Gaussian mixture models
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
SMEM Algorithm for Mixture Models
Neural Computation
Stochastic correlative learning algorithms
IEEE Transactions on Signal Processing
Automating the design of informative sequences of sensory stimuli
Journal of Computational Neuroscience
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We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal of learning the Neurocompensator parameters, we use a gradient-free optimization procedure, an improved version of the ALOPEX that we have developed (Haykin, Chen, & Becker, 2004), to learn the unknown parameters of the Neurocompensator. We present our methodology, learning procedure, and experimental results in detail; discussion is also given regarding the unsupervised learning and optimization methods.