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).

中文翻译：

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非线性声学回声消除的半盲源分离

当利用非线性自适应滤波器时，数值模型与实际非线性模型之间的失配是对非线性声学回声消除（NAEC）的挑战。为了缓解这个问题，我们将无记忆非线性的基本通用扩展合并为半盲源分离（SBSS）。通过将远端输入信号的所有基本函数视为已知的等效参考信号，遵循约束的缩放自然梯度策略，得出了SBSS更新算法。与常用的自适应算法不同，所提出的SBSS基于近端信号和参考信号之间的独立性，并且对数值模型和实际模型之间的非线性失配较不敏感。