20 citations · 36 across the 6 of their papers we have counts for
8 papers
SpectroStream: A Versatile Neural Codec for General Audio
Yunpeng Li, Kehang Han, Brian McWilliams +2
We propose SpectroStream, a full-band multi-channel neural audio codec. Successor to the well-established SoundStream, SpectroStream extends its capability beyond 24 kHz monophonic…
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…
MicAugment: One-shot Microphone Style Transfer
Zalán Borsos, Yunpeng Li, Beat Gfeller +1
A crucial aspect for the successful deployment of audio-based models "in-the-wild" is the robustness to the transformations introduced by heterogeneous acquisition conditions. In t…
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…
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…
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…