System identification: theory for the user
System identification: theory for the user
Applications of digital signal processing to audio and acoustics
Applications of digital signal processing to audio and acoustics
Linear Prediction of Speech
Adaptive feedback cancellation for audio applications
Signal Processing
Multi-Pitch Estimation
Optimal filter designs for separating and enhancing periodic signals
IEEE Transactions on Signal Processing
Adaptive feedback cancellation in hearing aids with linear prediction of the desired signal
IEEE Transactions on Signal Processing - Part I
Asymptotic MAP criteria for model selection
IEEE Transactions on Signal Processing
Acoustic feedback cancellation for long acoustic paths using a nonstationary source model
IEEE Transactions on Signal Processing
A Pole-Zero Placement Technique for Designing Second-Order IIR Parametric Equalizer Filters
IEEE Transactions on Audio, Speech, and Language Processing
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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.