ML Anthology

Hypernetworks Build Implicit Neural Representations of Sounds

Filip Szatkowski, Karol J. Piczak, Przemyslaw Spurek, Jacek Tabor, Tomasz Trzcinski
ECML-PKDD 2023 pp. 661-676
doi:10.1007/978-3-031-43421-1_39 /ecmlpkdd/2023/szatkowski2023ecmlpkdd-hypernetworks/

Abstract

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. Our code is publicly available at https://github.com/WUT-AI/hypersound .

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Cite

Text

Szatkowski et al. "Hypernetworks Build Implicit Neural Representations of Sounds." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. doi:10.1007/978-3-031-43421-1_39

Markdown

[Szatkowski et al. "Hypernetworks Build Implicit Neural Representations of Sounds." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023.](https://mlanthology.org/ecmlpkdd/2023/szatkowski2023ecmlpkdd-hypernetworks/) doi:10.1007/978-3-031-43421-1_39

BibTeX

@inproceedings{szatkowski2023ecmlpkdd-hypernetworks,
  title     = {{Hypernetworks Build Implicit Neural Representations of Sounds}},
  author    = {Szatkowski, Filip and Piczak, Karol J. and Spurek, Przemyslaw and Tabor, Jacek and Trzcinski, Tomasz},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2023},
  pages     = {661-676},
  doi       = {10.1007/978-3-031-43421-1_39},
  url       = {https://mlanthology.org/ecmlpkdd/2023/szatkowski2023ecmlpkdd-hypernetworks/}
}