论文标题
非线性声学回声取消的半盲源分离
Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation
论文作者
论文摘要
当使用非线性自适应滤波器时，数值和实际非线性模型之间的不匹配是非线性声音回声取消（NAEC）的挑战。为了减轻这个问题，我们将无内存非线性的基础生成扩展结合到半盲源分离（SBSS）中。通过将远端输入信号作为已知等效的参考信号的所有基本函数，SBSS更新算法是按照受限的自然梯度策略得出的。与常用的自适应算法不同，所提出的SBS基于近端信号和参考信号之间的独立性，并且对数值和实际模型之间的非线性不匹配不敏感。实验结果表明，该方法在回声损失增强（ERLE）和近端语音质量方面优于传统方法，这些方法通过语音质量的感知评估（PESQ）和短期客观清晰度（STOI）评估。
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).
