Scaling Up Adaptive Filter Optimizers
<|reference_start|>Scaling Up Adaptive Filter Optimizers: 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.<|reference_end|>
@article{casebeer2024scaling,
    title={Scaling Up Adaptive Filter Optimizers},
    author={Jonah Casebeer and Nicholas J. Bryan and Paris Smaragdis},
    journal={arXiv preprint arXiv:2403.00977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00977},
    primaryClass={cs.SD eess.AS}
}