297 citations · 1.2k across the 31 of their papers we have counts for
11 papers · 1 filter
Text-Driven Separation of Arbitrary Sounds
Kevin Kilgour, Beat Gfeller, Qingqing Huang +3
We propose a method of separating a desired sound source from a single-channel mixture, based on either a textual description or a short audio sample of the target source. This is…
CycleGAN-Based Unpaired Speech Dereverberation
Hannah Muckenhirn, Aleksandr Safin, Hakan Erdogan +4
Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance. The main limitati…
Real time spectrogram inversion on mobile phone
Oleg Rybakov, Marco Tagliasacchi, Yunpeng Li +3
We present two methods of real time magnitude spectrogram inversion: streaming Griffin Lim(GL) and streaming MelGAN. We demonstrate the impact of looking ahead on perceptual qualit…
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…
Learning to Denoise Historical Music
Yunpeng Li, Beat Gfeller, Marco Tagliasacchi +1
We propose an audio-to-audio neural network model that learns to denoise old music recordings. Our model internally converts its input into a time-frequency representation by means…
Towards Learning a Universal Non-Semantic Representation of Speech
Joel Shor, Aren Jansen, Ronnie Maor +7
The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a pre-existing embedding model trained for different datasets or tasks. The visual and l…
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…
Self-supervised audio representation learning for mobile devices
Marco Tagliasacchi, Beat Gfeller, Félix de Chaumont Quitry +1
We explore self-supervised models that can be potentially deployed on mobile devices to learn general purpose audio representations. Specifically, we propose methods that exploit t…