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eess.AS2026
UNITE-AUDIO: Joint Learning of Continuous Tokenization and Latent Flow Matching for Text-to-Audio Generation
Runwu Shi, Kai Li, Yujin Wang +8
Text-to-audio (TTA) generation aims to synthesize realistic audio that faithfully reflects natural-language descriptions. Most TTA systems adopt a two-stage latent paradigm: an aud…
eess.AS2026
Exploring Efficient Waveform Diffusion Models for Foley Sound Generation
Runwu Shi, Chang Li, Jiahui Li +7
Recent advances in diffusion models have enabled high-fidelity Foley sound generation directly in the waveform space. Existing waveform diffusion models primarily rely on time-doma…
eess.AS2025
Unsupervised Single-Channel Audio Separation with Diffusion Source Priors
Runwu Shi, Chang Li, Jiang Wang +5
Single-channel audio separation aims to separate individual sources from a single-channel mixture. Most existing methods rely on supervised learning with synthetically generated pa…