43 citations · 110 across the 5 of their papers we have counts for
5 papers
AudioPaLM: A Large Language Model That Can Speak and Listen
Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen +27
We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and…
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…
LEAF: A Learnable Frontend for Audio Classification
Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry +1
Mel-filterbanks are fixed, engineered audio features which emulate human perception and have been used through the history of audio understanding up to today. However, their undeni…
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…
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…