Active noise control (ANC) technology is increasingly ubiquitous in wearable audio devices, or hearables. Owing to its low computational complexity, high robustness, and exemplary performance in dealing with dynamic noise, the fixed-coefficient control filter strategy plays a central role in portable ANC implementation. Unlike its traditional adaptive counterpart, the fixed-filter strategy is unable to attain optimal noise reduction for different types of noise. Hence, we propose a selective fixed-filter ANC method based on a simplified two-dimensional convolution neural network (2D CNN), which is implemented on a co-processor (e.g., in a mobile phone), to derive the most suitable control filter for different noise types. To further reduce classification complexity, we designed a lightweight one-dimensional CNN (1D CNN), which can directly classify noise types in time domain. A numerical simulation based on measured paths in headphones demonstrates the proposed algorithm’s efficacy in attenuating real-world non-stationary noise over conventional adaptive algorithms.

主动噪声控制 (ANC) 技术在可穿戴音频设备或耳戴式设备中越来越普遍。由于其低计算复杂度、高鲁棒性和处理动态噪声的示范性能，固定系数控制滤波器策略在便携式 ANC 实现中发挥着核心作用。与其传统的自适应对应物不同，固定滤波器策略无法针对不同类型的噪声实现最佳降噪。因此，我们提出了一种基于简化二维卷积神经网络 (2D CNN) 的选择性固定滤波器 ANC 方法，该方法在协处理器（例如，在手机中）上实现，以推导出最合适的控制滤波器针对不同的噪音类型。为了进一步降低分类复杂度，我们设计了一个轻量级的一维 CNN（1D CNN），可以直接在时域上对噪声类型进行分类。基于耳机中测量路径的数值模拟证明了与传统自适应算法相比，所提出的算法在衰减现实世界非平稳噪声方面的功效。