120 citations · 146 across the 4 of their papers we have counts for
7 papers
PoDAR: Power-Disentangled Audio Representation for Generative Modeling
Alejandro Luebs, Mithilesh Vaidya, Ishaan Kumar +5
The performance of audio latent diffusion models is primarily governed by generator expressivity and the modelability of the underlying latent space. While recent research has focu…
High-Fidelity Audio Compression with Improved RVQGAN
Rithesh Kumar, Prem Seetharaman, Alejandro Luebs +2
Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model…
SoundStream: An End-to-End Neural Audio Codec
Neil Zeghidour, Alejandro Luebs, Ahmed Omran +2
We present SoundStream, a novel neural audio codec that can efficiently compress speech, music and general audio at bitrates normally targeted by speech-tailored codecs. SoundStrea…
Handling Background Noise in Neural Speech Generation
Tom Denton, Alejandro Luebs, Felicia S. C. Lim +4
Recent advances in neural-network based generative modeling of speech has shown great potential for speech coding. However, the performance of such models drops when the input is n…
Generative Speech Coding with Predictive Variance Regularization
W. Bastiaan Kleijn, Andrew Storus, Michael Chinen +5
The recent emergence of machine-learning based generative models for speech suggests a significant reduction in bit rate for speech codecs is possible. However, the performance of…
Low Bit-Rate Speech Coding with VQ-VAE and a WaveNet Decoder
Cristina Gârbacea, Aäron van den Oord, Yazhe Li +4
In order to efficiently transmit and store speech signals, speech codecs create a minimally redundant representation of the input signal which is then decoded at the receiver with…