System identification: theory for the user
System identification: theory for the user
Pseudo-multi-tap pitch filters in a low bit-rate CELP speech coder
Speech Communication
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Signal processing for in-car communication systems
Signal Processing
Comparison of linear prediction models for audio signals
EURASIP Journal on Audio, Speech, and Music Processing
Adaptive feedback cancellation in hearing aids with linear prediction of the desired signal
IEEE Transactions on Signal Processing - Part I
A stable and efficient adaptive notch filter for direct frequencyestimation
IEEE Transactions on Signal Processing
Feedback cancellation in hearing aids: results from a computersimulation
IEEE Transactions on Signal Processing
Double-Talk-Robust Prediction Error Identification Algorithms for Acoustic Echo Cancellation
IEEE Transactions on Signal Processing
An adaptive notch filter with improved tracking properties
IEEE Transactions on Signal Processing
Tracking of a time-varying acoustic impulse response by an adaptivefilter
IEEE Transactions on Signal Processing
Acoustic feedback cancellation for long acoustic paths using a nonstationary source model
IEEE Transactions on Signal Processing
Unbiased adaptive feedback cancellation in hearing aids by closed-loop identification
IEEE Transactions on Audio, Speech, and Language Processing
Closed-loop identification revisited
Automatica (Journal of IFAC)
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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.