HyperGANStrument：基于音高不变超网络的乐器声音合成与编辑
HyperGANStrument: Instrument Sound Synthesis and Editing with Pitch-Invariant Hypernetworks
AI总结：
提出HyperGANStrument，通过音高不变超网络调制预训练GANStrument生成器权重，并结合对抗性微调方案，显著提升了乐器声音的重建保真度、生成多样性与可编辑性。
摘要：
GANStrument利用具有音高不变特征提取器和实例条件技术的GAN，在合成逼真的乐器声音方面展现了卓越能力。为进一步提高重建能力和音高准确性以增强用户提供声音的可编辑性，我们提出了HyperGANStrument，该模型引入了音高不变的超网络，以单样本声音为输入来调制预训练的GANStrument生成器的权重。超网络调制为生成器在重建输入声音时提供反馈。此外，我们利用针对超网络的对抗性微调方案，以提高生成器的重建保真度和生成多样性。实验结果表明，所提出的模型不仅增强了GANStrument的生成能力，还显著提升了合成声音的可编辑性。音频示例可在在线演示页面获取。
英文摘要：
GANStrument, exploiting GANs with a pitch-invariant feature extractor and instance conditioning technique, has shown remarkable capabilities in synthesizing realistic instrument sounds. To further improve the reconstruction ability and pitch accuracy to enhance the editability of user-provided sound, we propose HyperGANStrument, which introduces a pitch-invariant hypernetwork to modulate the weights of a pre-trained GANStrument generator, given a one-shot sound as input. The hypernetwork modulation provides feedback for the generator in the reconstruction of the input sound. In addition, we take advantage of an adversarial fine-tuning scheme for the hypernetwork to improve the reconstruction fidelity and generation diversity of the generator. Experimental results show that the proposed model not only enhances the generation capability of GANStrument but also significantly improves the editability of synthesized sounds. Audio examples are available at the online demo page.
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。
