89 citations · 231 across the 12 of their papers we have counts for
14 papers
AudioLM: a Language Modeling Approach to Audio Generation
Zalán Borsos, Raphaël Marinier, Damien Vincent +8
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio genera…
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…
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…
From Here to There: Video Inbetweening Using Direct 3D Convolutions
Yunpeng Li, Dominik Roblek, Marco Tagliasacchi
We consider the problem of generating plausible and diverse video sequences, when we are only given a start and an end frame. This task is also known as inbetweening, and it belong…
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…
Fréchet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms
Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek +1
We propose the Fréchet Audio Distance (FAD), a novel, reference-free evaluation metric for music enhancement algorithms. We demonstrate how typical evaluation metrics for speech en…
An Empirical Study of Generative Models with Encoders
Paul K. Rubenstein, Yunpeng Li, Dominik Roblek
Generative adversarial networks (GANs) are capable of producing high quality image samples. However, unlike variational autoencoders (VAEs), GANs lack encoders that provide the inv…
Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition
David B. Ramsay, Kevin Kilgour, Dominik Roblek +1
Low power digital signal processors (DSPs) typically have a very limited amount of memory in which to cache data. In this paper we develop efficient bottleneck feature (BNF) extrac…
Now Playing: Continuous low-power music recognition
Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo +8
Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entir…