A VSS sparseness controlled algorithm for feedback suppression in hearing aids

摘要

The widely used least mean square (LMS) and normalized LMS (NLMS) algorithm for the development of adaptive feedback canceller in hearing aid exhibits poor convergence in the presence of colored signal. However, more often the input signals available to the hearing aid are colored in nature, which results in degraded performance of the feedback canceller and loss of speech intelligibility. The affine projection algorithms (APAs) such as memorized improved proportionate APA (MIPAPA), simplified approximated MIPAPA (SAMIPAPA) offer faster convergence than the NLMS algorithm, especially for speech signals. This paper proposes a variable step size, sparseness controlled SAMIPAPA (VSS-SC-SAMIPAPA) algorithm for feedback cancellation in hearing aids. The performance of this algorithm for feedback cancellation in hearing aids is measured in terms of misalignment and added stable gain (ASG) for both white noise and speech segment as input.