TY - JOUR
T1 - Improved prediction error filters for adaptive feedback cancellation in hearing aids
AU - Ngo, Kim
AU - van Waterschoot, Toon
AU - Christensen, Mads Græsbøll
AU - Moonen, Marc
AU - Jensen, Søren Holdt
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
U2 - 10.1016/j.sigpro.2013.03.042
DO - 10.1016/j.sigpro.2013.03.042
M3 - Journal article
SN - 0165-1684
VL - 93
SP - 3062
EP - 3075
JO - Signal Processing
JF - Signal Processing
IS - 11
ER -