The gradual adaptation and possibility of divergence hinder the active noise control system from being applied to a wider range of applications. Selective active noise control has been proposed to rapidly reduce noise by selecting a pre-trained control filter for different primary noise detected without an error microphone. For stationary noise, considerable noise reduction performance with a short selection period is obtained. For non-stationary noise, more restrictive requirements are imposed on instant convergence, as it leads to faster tracking and better noise reduction performance. To speed up a selective filtered active noise control system, empirical wavelet transform is introduced here to accurately and instantaneously extract the frequency information of primary noise. The boundary of the first intrinsic mode function of random noises is extracted as the instant signal feature. Primary noise is attenuated immediately by picking the optimal pre-trained control filter labeled by the nearest boundary. The storage requirement for a pre-trained control filter library is reduced. Instant control is obtained, and the instability caused by output saturation is overcome. With more concentrated energy distribution, better noise reduction performance is achieved by the proposed algorithm compared to conventional and selective active noise control algorithms. Simulation results validate these advantages of the proposed algorithm.

中文翻译：

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使用经验小波变换来加速选择性滤波有源噪声控制系统。

渐进的适应和发散的可能性阻碍了有源噪声控制系统被应用到更广泛的应用中。已经提出了选择性有源噪声控制以通过为检测到的不同主噪声选择一个预训练的控制滤波器来快速减少噪声，而没有误差麦克风。对于平稳噪声，可以在较短的选择周期内获得可观的降噪性能。对于非平稳噪声，对即时收敛有更高的要求，因为它会导致更快的跟踪速度和更好的降噪性能。为了加快选择性滤波有源噪声控制系统的速度，本文引入经验小波变换，以准确，即时地提取一次噪声的频率信息。提取随机噪声的第一本征模式函数的边界作为即时信号特征。通过选择以最近边界标记的最佳预训练控制滤波器，可以立即衰减一次噪声。减少了对预训练的控制滤波器库的存储需求。获得即时控制，并克服了由输出饱和引起的不稳定性。与常规的和选择性的主动噪声控制算法相比，通过更集中的能量分配，所提出的算法可实现更好的降噪性能。仿真结果验证了所提出算法的这些优点。减少了对预训练的控制滤波器库的存储需求。获得即时控制，并克服了由输出饱和引起的不稳定性。与常规的和选择性的主动噪声控制算法相比，通过更集中的能量分配，所提出的算法可实现更好的降噪性能。仿真结果验证了所提出算法的这些优点。减少了对预训练的控制滤波器库的存储需求。获得即时控制，并克服了由输出饱和引起的不稳定性。与常规的和选择性的主动噪声控制算法相比，通过更集中的能量分配，所提出的算法可实现更好的降噪性能。仿真结果验证了所提出算法的这些优点。