Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space

Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space

Equalisation is one of the most commonly-used tools in sound production, allowing users to control the gains of different frequency components in an audio signal. In this paper we present a model for mapping a set of equalisation parameters to a reduced dimensionality space. The purpose of this approach is to allow a user to interact with the system in an intuitive way through both the reductio...

The Reduced Order Model Problem in Distributed Parameter Systems Adaptive

Dimensionality-reduced subspace clustering

Subspace clustering refers to the problem of clustering unlabeled high-dimensional data points into a union of low-dimensional linear subspaces, whose number, orientations, and dimensions are all unknown. In practice one may have access to dimensionality-reduced observations of the data only, resulting, e.g., from undersampling due to complexity and speed constraints on the acquisition device o...

Asymptotically Efficient Adaptive Allocation Schemes for Controlled I.I.D. Processes: Finite Parameter Space

Abstruct-We consider a controlled i.i.d. process whose distribution is parametrized by an unknown parameter 8 belonging to some known parameter space 8, and a one-step reward associated with each pair of control and the following state of the process. The objective is to maximize the expected value of the sum of one-step rewards over an infinite horizon. By introducing the loss associated with ...

Adaptive Bayesian space-time equalisation for multiple receive-antenna assisted single-input multiple-output systems

This contribution considers nonlinear space–time equalisation (STE) for multiple receive-antenna induced single-input multipleoutput (SIMO) systems. By exploiting the inherent symmetry of the underlying optimal Bayesian STE solution, a novel symmetric radial basis function (RBF) based STE scheme is proposed, which is capable of approaching the optimal Bayesian equalisation performance. Adaptive...