用神经网络压缩降噪滤波器空间，加速主动降噪收敛。
Latent FxLMS: Accelerating Active Noise Control with Neural Adaptive Filters
- 在隐空间更新滤波器权重，通过解码器生成实际滤波器。
- 特定条件下收敛步数更少，稳态误差与传统方法相当。
- 适合追求快速收敛的实时主动降噪系统应用。
滤波-X最小均方（FxLMS）常用于主动噪声控制（ANC），以在指定位置最小化声场。若已知噪声或控制源的空间分布范围，可通过沿自适应滤波器权重的低维流形进行优化来改进FxLMS。我们对从给定空间区域内采样的每个主源位置的稳态自适应滤波器系数训练一个自编码器，并将自适应滤波器权重约束为潜变量状态对应的解码器输出。随后，在潜空间执行更新，并使用解码器生成抵消滤波器。我们评估了不同神经网络约束和归一化技术对收敛速度与稳态均方误差的影响。在某些条件下，我们的潜空间FxLMS模型以更少步数收敛，且稳态误差与标准FxLMS相当。
原文摘要 · Abstract (English)
Filtered-X LMS (FxLMS) is commonly used for active noise control (ANC), wherein the soundfield is minimized at a desired location. Given prior knowledge of the spatial region of the noise or control sources, we could improve FxLMS by adapting along the low-dimensional manifold of possible adaptive filter weights. We train an auto-encoder on the filter coefficients of the steady-state adaptive filter for each primary source location sampled from a given spatial region and constrain the weights of the adaptive filter to be the output of the decoder for a given state of latent variables. Then, we perform updates in the latent space and use the decoder to generate the cancellation filter. We evaluate how various neural network constraints and normalization techniques impact the convergence speed and steady-state mean squared error. Under certain conditions, our Latent FxLMS model converges in fewer steps with comparable steady-state error to the standard FxLMS.
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