HyperGANStrument: Instrument Sound Synthesis and Editing with
  Pitch-Invariant Hypernetworks
Zhe Zhang Taketo Akama
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.