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.

HyperGANStrument通过引入一个与音调无关的超网络，来调节预训练的GANStrument生成器的权重，实现了对用户输入音频进行重建的能力的提升，并结合对超网络的敌对微调来改善生成器的重建准确性和多样性，从而不仅增强了GANStrument的生成能力，还显著提高了生成音频的可编辑性。

HyperGANStrument：使用无论音高如何均不变的超网络进行乐器声音合成与编辑