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20182026
most citedSeparate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data

14 citations · 29 across the 16 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.SD2021★ 7 cited

NICE-Beam: Neural Integrated Covariance Estimators for Time-Varying Beamformers

Jonah Casebeer, Jacob Donley, Daniel Wong +2

Estimating a time-varying spatial covariance matrix for a beamforming algorithm is a challenging task, especially for wearable devices, as the algorithm must compensate for time-va…

cs.SD2021

Auto-DSP: Learning to Optimize Acoustic Echo Cancellers

Jonah Casebeer, Nicholas J. Bryan, Paris Smaragdis

Adaptive filtering algorithms are commonplace in signal processing and have wide-ranging applications from single-channel denoising to multi-channel acoustic echo cancellation and…

cs.SD2021

Sound Event Detection with Adaptive Frequency Selection

Zhepei Wang, Jonah Casebeer, Adam Clemmitt +2

In this work, we present HIDACT, a novel network architecture for adaptive computation for efficiently recognizing acoustic events. We evaluate the model on a sound event detection…

cs.SD2021★ 14 cited

Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data

Efthymios Tzinis, Jonah Casebeer, Zhepei Wang +1

We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients. We simulate a r…

eess.AS2021

Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders

Jonah Casebeer, Vinjai Vale, Umut Isik +3

Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output…