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Advances in Machine Learning for DSP
Machine learning has advanced to the point where it is now feasible to incorporate it directly into DSP applications. The major advantage is in the ability for DSP algorithms to "learn" to perform well. In this talk we will discuss how methods such as nonlinear and non-Gaussian inference and Bayesian nonparametrics can be exploited to develop novel DSP algorithms with a much higher level of specificity and efficiency than classical LTI methods.
