38 citations · 90 across the 14 of their papers we have counts for
18 papers
Scaling Up Adaptive Filter Optimizers
Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis
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-dela…
Meta-Learning for Adaptive Filters with Higher-Order Frequency Dependencies
Junkai Wu, Jonah Casebeer, Nicholas J. Bryan +1
Adaptive filters are applicable to many signal processing tasks including acoustic echo cancellation, beamforming, and more. Adaptive filters are typically controlled using algorit…
Style Transfer of Audio Effects with Differentiable Signal Processing
Christian J. Steinmetz, Nicholas J. Bryan, Joshua D. Reiss
We present a framework that can impose the audio effects and production style from one recording to another by example with the goal of simplifying the audio production process. We…
Meta-AF: Meta-Learning for Adaptive Filters
Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis
Adaptive filtering algorithms are pervasive throughout signal processing and have had a material impact on a wide variety of domains including audio processing, telecommunications,…
Emotion Embedding Spaces for Matching Music to Stories
Minz Won, Justin Salamon, Nicholas J. Bryan +2
Content creators often use music to enhance their stories, as it can be a powerful tool to convey emotion. In this paper, our goal is to help creators find music to match the emoti…
Who calls the shots? Rethinking Few-Shot Learning for Audio
Yu Wang, Nicholas J. Bryan, Justin Salamon +2
Few-shot learning aims to train models that can recognize novel classes given just a handful of labeled examples, known as the support set. While the field has seen notable advance…