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Siamese SIREN: Audio Compression with Implicit Neural Representations

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arxiv 2306.12957 v1 pith:D7ZTAOUS submitted 2023-06-22 cs.SD cs.AIcs.LGeess.AS

Luca A. Lanzendörfer , Roger Wattenhofer This is my paper · ORCID
classification cs.SDcs.AIcs.LGeess.AS
keywords audiocompressionsireninrssiameseimplicitneuralrepresentations
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Data-driven Video Codec with Implicit Neural Representations

    eess.IV 2026-07 conditional novelty 6.0 of 10

    A single SIREN MLP can store a short video plus audio and be compressed to 2.33 MiB, but it remains far less efficient than H.264/HEVC/MP3 on every tested clip.

  2. Bias for Action: Video Implicit Neural Representations with Bias Modulation

    cs.CV 2025-01 conditional novelty 6.0 of 10

    Sharing an INR's weights across frames and modulating only per-frame biases via a time-conditioned hypernetwork gives a compact continuous video representation that outperforms prior video INRs on interpolation, super...

  3. EVOS: Efficient Implicit Neural Training via EVOlutionary Selector

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A sampling-based training strategy using cached loss ranking, frequency-guided crossover, and mutation reduces INR training time by 48-66% with no loss in quality.

  4. Enhancing Implicit Neural Representations via Symmetric Power Transformation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A reversible symmetric power transformation improves implicit neural representation fitting across audio, image, and video tasks with no added storage cost.

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