Employing sparsity criteria in linear prediction of speech has been proven successful for several analysis and coding purposes. However, sparse linear prediction comes at the expenses of a much higher computational burden and numerical sensitivity compared to the traditional minimum variance approach. This makes sparse linear prediction difficult to deploy in real-time systems. In this paper, we present a step towards real-time implementation of the sparse linear prediction problem using hand-tailored interior-point methods. Using compiled implementations the sparse linear prediction problems corresponding to a frame size of 20ms can be solved on a standard PC in approximately 2ms and orders faster than with general purpose software.
['Tobias Lindstrøm Jensen', 'Daniele Giacobello', 'Mads Græsbøll Christensen', 'Søren Holdt Jensen', 'Marc Moonen']
Real-time implementations of sparse linear prediction for speech processing