Improved prediction error filters for adaptive feedback cancellation in hearing aids

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 5–7 dB in MSG and up to 6–8 dB in terms of filter misadjustment.

• Different PEF designs for PEM-based AFC are proposed and evaluated.
• PEF design based on a more accurate near-end signal model improves AFC performance.
• Harmonic sinusoidal near-end signal models appear to be well suited for PEF design.
• Including voiced–unvoiced activity detector allows about 25% complexity reduction.
• 5–7 dB MSG and 6–8 dB misadjustment improvement over state-of-the-art PEM-based AFC.