Open AccessOn new efficient μ ‐law‐based method for feedback compensation in hearing aids
Author(s) -
Panda G.,
Puhan N.B.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.0483
Subject(s) - affine transformation , rate of convergence , computation , convergence (economics) , computer science , compensation (psychology) , algorithm , projection (relational algebra) , control theory (sociology) , artificial intelligence , mathematics , telecommunications , channel (broadcasting) , control (management) , psychology , psychoanalysis , pure mathematics , economics , economic growth
The affine‐projection‐like (APL) algorithm is reported to achieve lower computations than affine‐projection algorithm (APA) without compromising the steady‐state performance. Further, the performance accuracy of the adaptive feedback canceller (AFC) in hearing aids is enhanced using an improved proportionate APL (IPAPL) algorithm. Two new learning algorithms are proposed for AFC, which apply the memory of previous gain factors and μ ‐law proportionate technique to the IPAPL, termed as memorised IPAPL (MIPAPL) and μ ‐law MIPAPL (MMIPAPL), respectively. In addition, a segmented approach is also suggested which offers computational advantage over MMIPAPL. The results obtained from simulation‐based experiments demonstrate that the proposed methods achieve faster convergence rate than the existing methods.