27 citations · 103 across the 7 of their papers we have counts for
12 papers
One-shot conditional audio filtering of arbitrary sounds
Beat Gfeller, Dominik Roblek, Marco Tagliasacchi
We consider the problem of separating a particular sound source from a single-channel mixture, based on only a short sample of the target source. Using SoundFilter, a wave-to-wave…
Real-time Speech Frequency Bandwidth Extension
Yunpeng Li, Marco Tagliasacchi, Oleg Rybakov +2
In this paper we propose a lightweight model for frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz while restoring the high freq…
SEANet: A Multi-modal Speech Enhancement Network
Marco Tagliasacchi, Yunpeng Li, Karolis Misiunas +1
We explore the possibility of leveraging accelerometer data to perform speech enhancement in very noisy conditions. Although it is possible to only partially reconstruct user's spe…
Training Keyword Spotters with Limited and Synthesized Speech Data
James Lin, Kevin Kilgour, Dominik Roblek +1
With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tas…
Learning audio representations via phase prediction
Félix de Chaumont Quitry, Marco Tagliasacchi, Dominik Roblek
We learn audio representations by solving a novel self-supervised learning task, which consists of predicting the phase of the short-time Fourier transform from its magnitude. A co…
SPICE: Self-supervised Pitch Estimation
Beat Gfeller, Christian Frank, Dominik Roblek +3
We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations…