The paper introduces MAGNET, a masked generative sequence modeling method that operates directly over several streams of audio tokens. Unlike previous works, MAGNET is a single-stage, non-autoregressive transformer. During training,…
Continue readingThe article discusses Auffusion, a Text-to-Audio (TTA) system that leverages the power of diffusion models and large language models. Auffusion adapts Text-to-Image (T2I) diffusion models to the TTA task, improving…
Continue readingThe article discusses the development of the Balanced SNR-Aware (BSA) method, a technique designed to improve text-to-audio generation tasks. Diffusion models have shown potential in these tasks, but practical use…
Continue readingThe paper addresses the issue of distributional shift in machine learning models, especially in text-to-audio generation. The authors observe a consistent audio quality degradation in generated audio samples with user…
Continue readingThe research paper discusses the challenge in text-to-audio (TTA) generation, particularly the issue of under-specified user prompts when compared to the training text descriptions. The authors treat TTA models as…
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