User-Driven Quality Enhancement for Audio Signal Processing
Classical methods for audio and speech enhancement 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 of the system. In order to meet subjective specifications, we put forward the idea of a user-driven approach to audio enhancement through the inclusion in the optimization stage of an interactive evolutionary algorithm (IEA). In this way, performance of the system can be adapted to any user in a principled and systematic way, thus reflecting the desired subjective quality. Experiments in the context of echo cancellation support the proposed methodology, showing significant statistical advantage of the proposed framework with respect to classical approaches.
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