Interactive quality enhancement in acoustic echo cancellation

Classical adaptive algorithms for acoustic echo cancellation (AEC) are often based on error-driven optimization strategies, such as the mean-square error minimization. However, these approaches do not always satisfy the quality requirements demanded by users that avail of such audio signal processing systems. In order to meet subjective specifications, in this paper we put forward the idea of a user-driven approach to echo cancellation through the inclusion of an interactive evolutionary algorithm (IEA) in the optimization stage. As a consequence, performance of an AEC system can be adapted to any user preferences in a principled and systematic way, thus reflecting the desired subjective quality. Experiments in the context of AEC prove the effectiveness of the proposed methodology in enhancing the processed signal quality and show significant statistical advantages of the proposed framework with respect to classical approaches.