扩展自适应滤波器优化器的规模
Scaling Up Adaptive Filter Optimizers
AI总结：
本文提出监督式多步自适应滤波器SMS-AF，通过神经网络优化频域滤波器并引入剪枝、监督损失和多步更新，实现随计算与模型容量扩展的实时AEC及语音增强性能。
摘要：
我们提出了一种新的在线自适应滤波方法，称为监督式多步自适应滤波器（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，并能够以增加计算量为代价灵活地扩展性能——这一特性在自适应滤波器（AF）文献中很少被讨论，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、监督损失以及每个时间帧执行多个优化步骤。这些改进协同工作，从而解锁规模扩展能力。此外，我们还说明了该方法与卡尔曼滤波和元自适应滤波的关系，使其能够无缝适用于多种AF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比较。结果表明，该方法的性能随推理成本和模型容量的增加而提升，在两项任务上均取得数dB的性能增益，并且能够在单个CPU核心上实时运行。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
