In audio-related creative tasks, sound designers often seek to extend and morph different sounds from their libraries. Generative audio models, capable of creating audio using examples as references, offer promising solutions. By masking the noisy latents of a DiT and applying a novel variant of classifier-free guidance on such masked latents, we demonstrate that: (i) given an audio reference, we can extend it both forward and backward for a specified duration, and (ii) given two audio references, we can morph them seamlessly for the desired duration. Furthermore, we show that by fine-tuning the model on different types of stationary audio data we mitigate potential hallucinations. The effectiveness of our method is supported by objective metrics, with the generated audio achieving Fréchet Audio Distances (FADs) comparable to those of real samples from the training data. Additionally, we validate our results through a subjective listener test, where subjects gave positive ratings to the proposed model generations. This technique paves the way for more controllable and expressive generative sound frameworks, enabling sound designers to focus less on tedious, repetitive tasks and more on their actual creative process.

翻译：在音频相关的创意任务中，声音设计师常常需要从他们的音效库中扩展和变形不同的声音。能够以示例为参考生成音频的生成式音频模型为此提供了有前景的解决方案。通过对DiT的噪声潜在表示进行掩码，并在这些掩码后的潜在表示上应用一种新颖的无分类器引导变体，我们证明了：（i）给定一个音频参考，我们可以将其向前和向后扩展指定的时长；（ii）给定两个音频参考，我们可以将它们无缝变形为期望的时长。此外，我们表明，通过在不同类型的平稳音频数据上对模型进行微调，可以减轻潜在的幻觉问题。我们方法的有效性得到了客观指标的支持，生成音频的Fréchet音频距离（FADs）与训练数据中真实样本的FADs相当。此外，我们通过主观听音测试验证了我们的结果，受试者对所提模型生成的音频给出了积极的评价。这项技术为开发更具可控性和表现力的生成式声音框架铺平了道路，使声音设计师能够减少对繁琐、重复性任务的关注，而更多地投入到实际的创作过程中。