Scaling Up Adaptive Filter Optimizers
arxiv(2024)
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.
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