# Siamese SIREN: Audio Compression with Implicit Neural Representations

arXiv: https://arxiv.org/abs/2306.12957 · HTML: https://mlsanity.com/paper/2306.12957/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [pixelNeRF: Neural Radiance Fields from One or Few Images](https://mlsanity.com/paper/2012.02190/) (2020)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [COIN: COmpression with Implicit Neural representations](https://mlsanity.com/paper/2103.03123/) (2021)
- [Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms](https://mlsanity.com/paper/1812.08466/) (2018)
- [Implicit Neural Representations for Image Compression](https://mlsanity.com/paper/2112.04267/) (2021)
- [ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric](https://mlsanity.com/paper/2004.09584/) (2020)
- [Seeing Implicit Neural Representations as Fourier Series](https://mlsanity.com/paper/2109.00249/) (2021)
- [CDPAM: Contrastive learning for perceptual audio similarity](https://mlsanity.com/paper/2102.05109/) (2021)
- [Evaluating generative audio systems and their metrics](https://mlsanity.com/paper/2209.00130/) (2022)
- [HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks](https://mlsanity.com/paper/2211.01839/) (2022)
- [Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations](https://mlsanity.com/paper/2111.08462/) (2021)

## Built on by (most cited first, 13 of 13)

- [C3: High-performance and low-complexity neural compression from a single image or video](https://mlsanity.com/paper/2312.02753/) (2023)
- [HOSC: A Periodic Activation Function for Preserving Sharp Features in Implicit Neural Representations](https://mlsanity.com/paper/2401.10967/) (2024)
- [Learning Transferable Features for Implicit Neural Representations](https://mlsanity.com/paper/2409.09566/) (2024)
- [EVOS: Efficient Implicit Neural Training via EVOlutionary Selector](https://mlsanity.com/paper/2412.10153/) (2024)
- [Bias for Action: Video Implicit Neural Representations with Bias Modulation](https://mlsanity.com/paper/2501.09277/) (2025)
- [Spectral Bottleneck in Sinusoidal Representation Networks: Noise is All You Need](https://mlsanity.com/paper/2509.09719/) (2025)
- [Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations](https://mlsanity.com/paper/2404.04439/) (2024)
- [The Surprising Effectiveness of Noise Pretraining for Implicit Neural Representations](https://mlsanity.com/paper/2603.29034/) (2026)
- [Implicit Neural Image Field for Biological Microscopy Image Compression](https://mlsanity.com/paper/2405.19012/) (2024)
- [As Good as It KAN Get: High-Fidelity Audio Representation](https://mlsanity.com/paper/2503.02585/) (2025)
- [What Cohort INRs Encode and Where to Freeze Them](https://mlsanity.com/paper/2605.08298/) (2026)
- [Data-driven Video Codec with Implicit Neural Representations](https://mlsanity.com/paper/2607.15298/) (2026)
- [GS-Codec: A Gaussian-Splatting Bottleneck for Neural Audio Coding](https://mlsanity.com/paper/2610.04651/) (2026)

## Abstract (arXiv)

Implicit Neural Representations (INRs) have emerged as a promising method for representing diverse data modalities, including 3D shapes, images, and audio. While recent research has demonstrated successful applications of INRs in image and 3D shape compression, their potential for audio compression remains largely unexplored. Motivated by this, we present a preliminary investigation into the use of INRs for audio compression. Our study introduces Siamese SIREN, a novel approach based on the popular SIREN architecture. Our experimental results indicate that Siamese SIREN achieves superior audio reconstruction fidelity while utilizing fewer network parameters compared to previous INR architectures.