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Abstract: Acoustic feedback occurs in many audio applications involving musical sound signals. However, research efforts in acoustic feedback control have mainly been focused on speech applications. Since sound quality is of prime importance in audio applications, a proactive approach to acoustic feedback control is preferred to avoid ringing, howling, and excessive reverberation. Adaptive feedback cancellation (AFC) using a prediction-error-method (PEM)-based approach is a promising proactive solution, but existing algorithms are again designed for speech applications only. We propose to replace the all-pole near-end speech signal model in the PEM-based approach with a cascade of two near-end signal models: a tonal components model and a noise components model. We derive the identifiability conditions for joint identification of the acoustic feedback path and the cascaded near-end signal models. Depending on the model structure that is used for the near-end tonal components, three different PEM-based AFC algorithms are considered. By applying some relevant model approximations, the computational overhead of the proposed algorithms compared to the normalized least mean squares (NLMS) algorithm can be reduced to 25% of the NLMS complexity. Simulation results for both room acoustic and hearing aid scenarios indicate a significant performance improvement in terms of the misadjustment and the maximum stable gain increase. [Copyright &y& Elsevier]

The standard continuous adaptation feedback cancellation algorithm for feedback suppression in hearing aids suffers from a large model error or bias if the received sound signal is spectrally colored. To reduce the bias in the feedback path estimate, we propose adaptive feedback cancellation techniques that are based on a closed-loop identification of the feedback path as well as the (auto-regressive) modeling of the desired signal. In general, both models are not simultaneously identifiable in the closed-loop system at hand. We show that—under certain conditions, e.g., if a delay is inserted in the forward path—identification of both models is indeed possible. Two classes of adaptive procedures for identifying the desired signal model and the feedback path are derived: a two-channel identification method as well as a prediction error method. In contrast to the two-channel identification method, the prediction error method allows use of different adaptation schemes for the feedback path and for the desired signal model and, hence, is found to be preferable for highly non-stationary sound signals. Simulation results demonstrate that the proposed techniques outperform the standard continuous adaptation algorithm if the conditions for identifiability are satisfied. [ABSTRACT FROM AUTHOR]

Abstract: Acoustic feedback is a well-known problem in hearing aids, caused by the undesired acoustic coupling between the hearing aid loudspeaker and microphone. Acoustic feedback produces annoying howling sounds and limits the maximum achievable hearing aid amplification. This paper is focused on adaptive feedback cancellation (AFC) where the goal is to adaptively model the acoustic feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the acoustic feedback path model is the correlation between the near-end signal and the loudspeaker signal caused by the closed signal loop, in particular when the near-end signal is spectrally colored as is the case for a speech signal. This paper adopts a prediction-error method (PEM)-based approach to AFC, which is based on the use of decorrelating prediction error filters (PEFs). We propose a number of improved PEF designs that are inspired by harmonic sinusoidal modeling and pitch prediction of speech signals. The resulting PEM-based AFC algorithms are evaluated in terms of the maximum stable gain (MSG), filter misadjustment, and computational complexity. Simulation results for a hearing aid scenario indicate an improvement up to 5–7dB in MSG and up to 6–8dB in terms of filter misadjustment. [Copyright &y& Elsevier]