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[2602.15082] S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization
Summary
The paper presents S-PRESSO, a novel sound effect compression model that achieves ultra-low bitrate audio compression using diffusion autoencoders, demonstrating significant improvements in audio quality at extreme compression rates.
Why It Matters
As audio streaming and storage demands grow, efficient compression techniques are crucial. S-PRESSO addresses the limitations of existing methods by enabling high-quality audio at extremely low bitrates, which can benefit various applications in multimedia and AI-driven audio technologies.
Key Takeaways
- S-PRESSO achieves audio compression down to 0.096 kbps with high quality.
- Utilizes a pretrained latent diffusion model for effective audio reconstruction.
- Demonstrates superior performance over existing audio compression methods.
- Operates at low frame rates, achieving a 750x compression rate.
- Addresses challenges of audible artifacts in low-bitrate audio.
Computer Science > Sound arXiv:2602.15082 (cs) [Submitted on 16 Feb 2026] Title:S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization Authors:Zineb Lahrichi (IP Paris), Gaëtan Hadjeres, Gaël Richard (IP Paris), Geoffroy Peeters (IP Paris) View a PDF of the paper titled S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization, by Zineb Lahrichi (IP Paris) and 3 other authors View PDF Abstract:Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have been applied to compression, pushing the limits of continuous and discrete approaches. However, existing methods remain constrained to low-resolution audio and degrade substantially at very low bitrates, where audible artifacts are prominent. In this paper, we present S-PRESSO, a 48kHz sound effect compression model that produces both continuous and discrete embeddings at ultra-low bitrates, down to 0.096 kbps, via offline quantization. Our model relies on a pretrained latent diffusion model to decode compressed audio embeddings learned by a latent encoder. Leveraging the generative priors of the diffusion decoder, we achieve extremely low frame rates, down to 1Hz (750x compression rate), producing convincing and realistic reconstructions at the cost of exact fidelity. Despite operating at high compression rates, we de...
