Document Type : Research Paper
Research Scholar, ECE Department, National Institute of Technology, Warangal, India
Professor, ECE Department, National Institute of Technology, Warangal, India
Acoustic coupling between the microphone and the loudspeaker is a major issue in open-fit digital hearing aids. When compared to a close-fit hearing aid, an open-fit dramatically reduces signal quality and limits the potential maximum stable gain. Adaptive feedback cancellation (AFC) is a practical method for reducing the influence of acoustic coupling. However, because to the high correlation between the loudspeaker signal and the incoming signal, it might induce bias in calculating the feedback path if not carefully considered, especially when the incoming signal is spectrally coloured, as in speech and music. For decreasing this bias, the prediction error method (PEM) is well recognized. In this paper, we proposed a simplified multi-structure Kalman filter for implementing PEM based AFC. Kalman filter allows further increase in convergence/tracking rates and the high computational complexity of generalized Kalman filter is reduced by multi-structured topology and this in turn reduce the computational complexity and also subband topology provide low processing delay.
The proposed acoustic feedback cancellation approach could benefit nanostructured materials research by enabling more precise audio measurements and characterization of nanomaterials. By reducing interference from acoustic feedback, the algorithms allow for higher gain settings and improved signal-to-noise ratios when analyzing acoustic responses of nanostructures. This could lead to more accurate measurement of nanostructure properties and dynamics.
To overcome the reconvergence inability of during the change in feedback path, switched combination of SMKF and NLMS is used. Simulation results showed that the proposed algorithm performed better.