mlsanityData to 14 Oct 2026

Siamese SIREN: Audio Compression with Implicit Neural Representations

arXiv 2306.12957 ↗First posted 22 Jun 2023cs.SD cs.AI cs.LGcited by 13

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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.

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

16 of 23 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  6. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,584
  7. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  8. 2021COIN: COmpression with Implicit Neural representationscited by 233
  9. 2018Fr'echet Audio Distance: A Metric for Evaluating Music Enhancement Algorithmscited by 225
  10. 2021Implicit Neural Representations for Image Compressioncited by 162
  11. 2020ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metriccited by 79
  12. 2021Seeing Implicit Neural Representations as Fourier Seriescited by 50
4 more
  1. 2021CDPAM: Contrastive learning for perceptual audio similaritycited by 42
  2. 2022Evaluating generative audio systems and their metricscited by 16
  3. 2022HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworkscited by 12
  4. 2021Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representationscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 3; 2026: 4.

13 papers.

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  1. 2023C3: High-performance and low-complexity neural compression from a single image or videocited by 55
  2. 2024HOSC: A Periodic Activation Function for Preserving Sharp Features in Implicit Neural Representationscited by 14
  3. 2024Learning Transferable Features for Implicit Neural Representationscited by 14
  4. 2024EVOS: Efficient Implicit Neural Training via EVOlutionary Selectorcited by 7
  5. 2025Bias for Action: Video Implicit Neural Representations with Bias Modulationcited by 2
  6. 2025Spectral Bottleneck in Sinusoidal Representation Networks: Noise is All You Needcited by 2
  7. 2024Rethinking Non-Negative Matrix Factorization with Implicit Neural Representationscited by 1
  8. 2026The Surprising Effectiveness of Noise Pretraining for Implicit Neural Representationscited by 1
  9. 2024Implicit Neural Image Field for Biological Microscopy Image Compressioncited by 0
  10. 2025As Good as It KAN Get: High-Fidelity Audio Representationcited by 0
  11. 2026What Cohort INRs Encode and Where to Freeze Themcited by 0
  12. 2026Data-driven Video Codec with Implicit Neural Representationscited by 0
1 more
  1. 2026GS-Codec: A Gaussian-Splatting Bottleneck for Neural Audio Codingcited by 0