We introduce a new online adaptive filtering method called supervised multi-step adaptive filters (SMS-AF). Our method uses neural networks to control or optimize linear multi-delay or multi-channel frequency-domain filters and can flexibly scale-up performance at the cost of increased compute -- a property rarely addressed in the AF literature, but critical for many applications. To do so, we extend recent work with a set of improvements including feature pruning, a supervised loss, and multiple optimization steps per time-frame. These improvements work in a cohesive manner to unlock scaling. Furthermore, we show how our method relates to Kalman filtering and meta-adaptive filtering, making it seamlessly applicable to a diverse set of AF tasks. We evaluate our method on acoustic echo cancellation (AEC) and multi-channel speech enhancement tasks and compare against several baselines on standard synthetic and real-world datasets. Results show our method performance scales with inference cost and model capacity, yields multi-dB performance gains for both tasks, and is real-time capable on a single CPU core.

翻译：我们提出一种新的在线自适应滤波方法，称为监督多步自适应滤波器（SMS-AF）。该方法利用神经网络控制或优化线性多延迟或多通道频域滤波器，并能以增加计算开销为代价灵活提升性能——这一特性在自适应滤波文献中鲜有探讨，但对许多应用至关重要。为此，我们在近期工作的基础上进行了一系列改进，包括特征剪枝、监督损失函数以及每时间帧的多步优化。这些改进协同作用以实现性能缩放。此外，我们展示了该方法与卡尔曼滤波和元自适应滤波的关联，使其能够无缝应用于多种自适应滤波任务。我们在声学回声消除（AEC）和多通道语音增强任务上评估了该方法，并在标准合成数据集和真实世界数据集上与多个基线进行了比较。结果表明，我们的方法性能随推理成本和模型容量扩展，在两个任务上均取得了多分贝的性能提升，且能在单CPU核心上实现实时处理。