Implicit Neural Representations (INRs) are nowadays used to represent
multimedia signals across various real-life applications, including image
super-resolution, image compression, or 3D rendering. Existing methods that
leverage INRs are predominantly focused on visual data, as their application to
other modalities, such as audio, is nontrivial due to the inductive biases
present in architectural attributes of image-based INR models. To address this
limitation, we introduce HyperSound, the first meta-learning approach to
produce INRs for audio samples that leverages hypernetworks to generalize
beyond samples observed in training. Our approach reconstructs audio samples
with quality comparable to other state-of-the-art models and provides a viable
alternative to contemporary sound representations used in deep neural networks
for audio processing, such as spectrograms.

HyperSound 是第一种利用超网络进行元学习的方法，用于生成适合于音频样本的 INRs，以解决现有方法难以应用于音频的局限性，提供了一种可行的代替深度神经网络中当前用于音频处理的声谱图表示方法，并且重新构建的音频样本质量可与其他最先进的模型相媲美。

超网构建音频的隐式神经表示

Hypernetworks build Implicit Neural Representations of Sounds

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.

本研究提出了一种基于超网络元学习的方法，用于生成未训练音频信号的隐式神经表示（INRs），并展示了其重建声波信号的优秀性能。