1 research outputs found
Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation
The mismatch between the numerical and actual nonlinear models is a challenge
to nonlinear acoustic echo cancellation (NAEC) when the nonlinear adaptive
filter is utilized. To alleviate this problem, we combine a basis-generic
expansion of the memoryless nonlinearity into semi-blind source separation
(SBSS). By regarding all the basis functions of the far-end input signal as the
known equivalent reference signals, an SBSS updating algorithm is derived
following the constrained scaled natural gradient strategy. Unlike the commonly
utilized adaptive algorithm, the proposed SBSS is based on the independence
between the near-end signal and the reference signals, and is less sensitive to
the mismatch of nonlinearity between the numerical and actual models.
Experimental results show that the proposed method outperforms conventional
methods in terms of echo return loss enhancement (ERLE) and near-end speech
quality evaluated by perceptual evaluation of speech quality (PESQ) and
short-time objective intelligibility (STOI)