  Implicit neural representations (INRs) are a rapidly growing research field,
which provides alternative ways to represent multimedia signals. Recent
applications of INRs include image super-resolution, compression of
high-dimensional signals, or 3D rendering. However, these solutions usually
focus on visual data, and adapting them to the audio domain is not trivial.
Moreover, it requires a separately trained model for every data sample. To
address this limitation, we propose HyperSound, a meta-learning method
leveraging hypernetworks to produce INRs for audio signals unseen at training
time. We show that our approach can reconstruct sound waves with quality
comparable to other state-of-the-art models.
