Author:

Keywords:

SISTA, Science & Technology, Technology, Engineering, Electrical & Electronic, Engineering, Loudspeaker compensation, audio quality, Hammerstein model, embedded optimization, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, Speech-Language Pathology & Audiology, 4006 Communications engineering, 4602 Artificial intelligence, 4603 Computer vision and multimedia computation

Abstract:

This paper presents an embedded-optimization-based loudspeaker precompensation algorithm using a Hammerstein loudspeaker model, i.e. a cascade of a memoryless nonlinearity and a linear finite impulse response filter. The loudspeaker precompensation consists in a per-frame signal optimization. In order to minimize the perceptible distortion incurred in the loudspeaker, a psychoacoustically motivated optimization criterion is proposed. The resulting per-frame signal optimization problems are solved efficiently using first-order optimization methods. Depending on the invertibility and the smoothness of the memoryless nonlinearity, different first-order optimization methods are proposed and their convergence properties are analyzed. Objective evaluation experiments using synthetic loudspeaker models and real loudspeakers show that the proposed loudspeaker precompensation algorithm provides a significant audio quality improvement, especially so at high playback levels.