Generative Audio Extension and Morphing
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel approach for generating high-quality audio extensions and morphs using Diffusion Transformers and a variant of classifier-free guidance. The technical contributions are significant, addressing real-world challenges faced by sound designers and demonstrating promising results through rigorous evaluation.
Methodology
The methodology presented in this paper is robust and innovative, leveraging Diffusion Transformers and a novel Audio Prompt Guidance technique to effectively extend and morph audio. The authors provide a clear description of their approach, including the masking function and the fine-tuning strategy using the Noise Floor Dataset to mitigate hallucinations. However, while the methodology is well-structured, it could benefit from a more detailed exploration of the limitations of the masking function and guidance techniques in varying audio contexts.
Results & evaluation
The experimental evaluation is comprehensive, employing both objective metrics (Fréchet Audio Distance) and subjective listener tests to validate the effectiveness of the proposed model. The use of a large dataset for training and the careful selection of evaluation clips from sound design professionals enhances the credibility of the results. However, the paper could improve by including more diverse audio samples and comparing against a broader range of existing methods.
Reproducibility
The paper provides sufficient detail on the architecture, training process, and evaluation metrics, which aids in reproducibility. However, the absence of specific code or model weights limits the ease with which other researchers can replicate the results. Including a GitHub repository or similar resource would significantly enhance reproducibility.
Limitations
The paper acknowledges the potential for hallucinations in generated audio, particularly with stationary sounds, and discusses the trade-off between reducing hallucinations and maintaining fidelity to the original prompts. However, it does not thoroughly address how the model performs with non-stationary sounds or in complex soundscapes, which could be a significant limitation for practical applications.
Broader impact
The proposed model has the potential to significantly impact the field of sound design by automating tedious tasks and enhancing the creative process for sound designers. The ability to generate high-quality audio extensions and morphs could streamline workflows in various industries, including film, gaming, and virtual reality. Furthermore, the methodology could inspire future research in generative audio models and their applications in other domains. The paper presents a novel approach for generating high-quality audio extensions and morphs using Diffusion Transformers and a variant of classifier-free guidance. The technical contributions are significant, addressing real-world challenges faced by sound designers and demonstrating promising results through rigorous evaluation.
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