Adaptive feedback cancellation for audio signals using a warped all-pole near-end signal model

Sound amplification systems having a closed signal loop often suffer from acoustic feedback, which limits the achievable amount of amplification and severely affects sound quality. A promising solution to the feedback problem consists in predicting the feedback signal using an adaptive filter, however, a bias is then introduced due to signal correlation. In speech applications, a prediction-error-method- based approach to adaptive feedback cancellation has proven to be capable of providing sufficient decorrelation without sacrificing speech quality. This approach, which is based on estimating an all-pole near-end signal model, appears to be unappropriate for musical audio signals because of their large degree of tonality. We propose a novel prediction-error-method-based adaptive feedback cancellation algorithm that features a frequency-warped all-pole near-end signal model, which is better suited for tonal audio signals. Simulation results show a doubling of the convergence speed, with only a relatively small increase in computational complexity.