Haocheng Liu — accepted AI-conference papers

2024

GLA-GRAD: A Griffin-Lim Extended Waveform Generation Diffusion Model

Haocheng Liu, Teysir Baoueb, Mathieu Fontaine, Jonathan Le Roux, Gaël Richard

ICASSP 2024accepted

Diffusion models are receiving a growing interest for a variety of signal generation tasks such as speech or music synthesis. WaveGrad, for example, is a successful diffusion model that conditionally uses the mel spectrogram to guide a diffusion process for the generation of high-fidelity audio. How…

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2024

SpecDiff-GAN: A Spectrally-Shaped Noise Diffusion GAN for Speech and Music Synthesis

Teysir Baoueb, Haocheng Liu, Mathieu Fontaine, Jonathan Le Roux, Gaël Richard

ICASSP 2024accepted

Generative adversarial network (GAN) models can synthesize high-quality audio signals while ensuring fast sample generation. However, they are difficult to train and are prone to several issues including mode collapse and divergence. In this paper, we introduce SpecDiff-GAN, a neural vocoder based o…

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