59 citations · 112 across the 14 of their papers we have counts for
5 papers · 1 filter
A Generative-First Neural Audio Autoencoder
Jonah Casebeer, Ge Zhu, Zhepei Wang +1
Neural autoencoders underpin generative models. Practical, large-scale use of neural autoencoders for generative modeling necessitates fast encoding, low latent rates, and a single…
Audio Generation Through Score-Based Generative Modeling: Design Principles and Implementation
Ge Zhu, Yutong Wen, Zhiyao Duan
Diffusion models have emerged as powerful deep generative techniques, producing high-quality and diverse samples in applications in various domains including audio. While existing…
MusicHiFi: Fast High-Fidelity Stereo Vocoding
Ge Zhu, Juan-Pablo Caceres, Zhiyao Duan +1
Diffusion-based audio and music generation models commonly perform generation by constructing an image representation of audio (e.g., a mel-spectrogram) and then convert it to audi…
Cacophony: An Improved Contrastive Audio-Text Model
Ge Zhu, Jordan Darefsky, Zhiyao Duan
Despite recent advancements, audio-text models still lag behind their image-text counterparts in scale and performance. In this paper, we propose to improve both the data scale and…
EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis
Ge Zhu, Yutong Wen, Marc-André Carbonneau +1
Audio diffusion models can synthesize a wide variety of sounds. Existing models often operate on the latent domain with cascaded phase recovery modules to reconstruct waveform. Thi…