MambaFoley：使用选择性状态空间模型生成Foley音效
MambaFoley: Foley Sound Generation using Selective State-Space Models
AI总结：
本文提出MambaFoley，首次将Mamba选择性状态空间模型与扩散模型结合用于Foley音效生成，并通过主客观实验对比现有先进模型验证其效果。
摘要：
深度学习的最新进展推动了音频内容生成技术的广泛应用，尤其是在各类任务中采用去噪扩散概率模型（DDPM）。其中，Foley音效合成因其在多媒体内容创作应用中的作用而受到特别关注。鉴于声音具有时间依赖特性，设计能够有效处理音频样本序列建模的生成模型至关重要。选择性状态空间模型（SSM）近期被提出，作为以往技术的一种有效替代方案，在计算复杂度更低的情况下展现出具有竞争力的性能。本文提出MambaFoley，这是一个基于扩散的模型；据我们所知，它是首个利用近期提出的名为Mamba的SSM来完成Foley音效生成任务的模型。为评估所提方法的有效性，我们采用客观分析和主观分析，将其与一个最先进的Foley音效生成模型进行比较。
英文摘要：
Recent advancements in deep learning have led to widespread use of techniques for audio content generation, notably employing Denoising Diffusion Probabilistic Models (DDPM) across various tasks. Among these, Foley Sound Synthesis is of particular interest for its role in applications for the creation of multimedia content. Given the temporal-dependent nature of sound, it is crucial to design generative models that can effectively handle the sequential modeling of audio samples. Selective State Space Models (SSMs) have recently been proposed as a valid alternative to previously proposed techniques, demonstrating competitive performance with lower computational complexity. In this paper, we introduce MambaFoley, a diffusion-based model that, to the best of our knowledge, is the first to leverage the recently proposed SSM known as Mamba for the Foley sound generation task. To evaluate the effectiveness of the proposed method, we compare it with a state-of-the-art Foley sound generative model using both objective and subjective analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
