用分频结构选最优滤波器，快速降噪且适应复杂噪声
Subband Architecture Aided Selective Fixed-Filter Active Noise Control
- 分频架构预存多段滤波器，按频段匹配最佳控制方案
- 实测显示收敛快、降噪强，对非均匀噪声也有效
- 适合需要实时降噪的工业或音频设备场景
前馈式选择性固定滤波方法通过分析参考信号的频谱特征，选取最合适的预训练控制滤波器，避免传统自适应算法收敛慢的问题。但该方法仅能处理有限类型噪声，当输入噪声具有非均匀功率谱密度时性能下降。为此，本文提出一种基于无延迟子带结构的新颖选择性固定滤波方案。离线训练阶段，针对不同频段预训练子带滤波器并存储于专用子滤波器数据库；在线控制阶段，利用多相FFT滤波器组分解输入噪声，通过频带匹配机制为每个子带信号分配最适配的控制滤波器，随后采用权重叠加技术将所有子带权重合并为全带滤波器，实现实时噪声抑制。实验结果表明，所提方案具备快速收敛、高效降噪及强鲁棒性，在更复杂的噪声环境下表现优异。
原文摘要 · Abstract (English)
The feedforward selective fixed-filter method selects the most suitable pre-trained control filter based on the spectral features of the detected reference signal, effectively avoiding slow convergence in conventional adaptive algorithms. However, it can only handle limited types of noises, and the performance degrades when the input noise exhibits non-uniform power spectral density. To address these limitations, this paper devises a novel selective fixed-filter scheme based on a delayless subband structure. In the off-line training stage, subband control filters are pre-trained for different frequency ranges and stored in a dedicated sub-filter database. During the on-line control stage, the incoming noise is decomposed using a polyphase FFT filter bank, and a frequency-band-matching mechanism assigns each subband signal the most appropriate control filter. Subsequently, a weight stacking technique is employed to combine all subband weights into a fullband filter, enabling real-time noise suppression. Experimental results demonstrate that the proposed scheme provides fast convergence, effective noise reduction, and strong robustness in handling more complicated noisy environments.
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