基于反向传播机制的多通道有源噪声控制的实现
Implementation of Multi-channel Active Noise Control based on Back-propagation Mechanism
摘要：
有源噪声控制（ANC）系统通过引入抗噪声来抑制低频噪声，从而有效地抑制低频噪声。在ANC系统中，滤波x最小均方（FxLMS）和滤波x归一化最小均方（FxNLMS）算法是用于自适应地调整控制滤波器的公知算法。通常需要多通道ANC系统来衰减大空间中的不需要的噪声。然而，多通道FxLMS（McFxLMS）和多通道FxNLMS（McFxNLMS）算法的开源实现仍然很少。因此，本文提出了一种简单有效的McFxLMS和McFxNLMS算法的实现方法。受神经网络训练过程中的反向传播过程的启发，McFxLMS和McFxNLMS算法可以通过自动推导机制来实现。我们使用PyTorch中的自动派生机制实现了这两个算法，并在GitHub上提供了源代码。该实现方法可以提高多通道ANC系统的实用性，有望在ANC应用中得到广泛应用。
英文摘要：
Active noise control (ANC) systems can efficiently attenuate low-frequency noises by introducing anti-noises to combine with the unwanted noises. In ANC systems, the filtered-x least mean square (FxLMS) and filtered-X normalized least-mean-square (FxNLMS) algorithm are well-known algorithms for adaptively adjusting control filters. Multi-channel ANC systems are typically required to attenuate unwanted noises in a large space. However, open-source implementations of the multi-channel FxLMS (McFxLMS) and multi-channel FxNLMS (McFxNLMS) algorithm continue to be scarce. Therefore, this paper proposes a simple and effective implementation approach of the McFxLMS and McFxNLMS algorithm. Motivated by the back-propagation process during neural network training, the McFxLMS and McFxNLMS algorithm can be implemented via automatic derivation mechanism. We implemented the two algorithms using the automatic derivation mechanism in PyTorch and made the source code available on GitHub. This implementation method can improve the practicality of multi-channel ANC systems, which is expected to be widely used in ANC applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
