5 code implementations • 7 Sep 2022 • Zalán Borsos, Raphaël Marinier, Damien Vincent, Eugene Kharitonov, Olivier Pietquin, Matt Sharifi, Dominik Roblek, Olivier Teboul, David Grangier, Marco Tagliasacchi, Neil Zeghidour
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
no code implementations • 4 Nov 2020 • Beat Gfeller, Dominik Roblek, Marco Tagliasacchi
When trained on Librispeech, our model achieves an SI-SDR improvement of 14. 0 dB when separating one voice from a mixture of two speakers.
1 code implementation • 4 Sep 2020 • Marco Tagliasacchi, Yunpeng Li, Karolis Misiunas, Dominik Roblek
We explore the possibility of leveraging accelerometer data to perform speech enhancement in very noisy conditions.
no code implementations • 5 Aug 2020 • Yunpeng Li, Beat Gfeller, Marco Tagliasacchi, Dominik Roblek
We propose an audio-to-audio neural network model that learns to denoise old music recordings.
no code implementations • 31 Jan 2020 • James Lin, Kevin Kilgour, Dominik Roblek, Matthew Sharifi
With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords.
Ranked #10 on Keyword Spotting on Google Speech Commands (Google Speech Commands V2 12 metric)
no code implementations • 25 Oct 2019 • Beat Gfeller, Christian Frank, Dominik Roblek, Matt Sharifi, Marco Tagliasacchi, Mihajlo Velimirović
We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation.
no code implementations • 25 Oct 2019 • 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.
1 code implementation • 24 May 2019 • Yunpeng Li, Dominik Roblek, Marco Tagliasacchi
We first obtain a latent video representation using a stochastic fusion mechanism that learns how to incorporate information from the start and end frames.
no code implementations • 24 May 2019 • Marco Tagliasacchi, Beat Gfeller, Félix de Chaumont Quitry, Dominik Roblek
We explore self-supervised models that can be potentially deployed on mobile devices to learn general purpose audio representations.
no code implementations • 19 Dec 2018 • Paul K. Rubenstein, Yunpeng Li, Dominik Roblek
Generative adversarial networks (GANs) are capable of producing high quality image samples.
no code implementations • 31 Oct 2018 • David B. Ramsay, Kevin Kilgour, Dominik Roblek, Matthew Sharifi
Low power digital signal processors (DSPs) typically have a very limited amount of memory in which to cache data.
no code implementations • 29 Nov 2017 • Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo, Kevin Kilgour, Sanjiv Kumar, James Lyon, Julian Odell, Marvin Ritter, Dominik Roblek, Matthew Sharifi, Mihajlo Velimirović
To reduce battery consumption, a small music detector runs continuously on the mobile device's DSP chip and wakes up the main application processor only when it is confident that music is present.