S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization
arXiv:2602.15082v1 Announce Type: cross 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
arXiv:2602.15082v1 Announce Type: cross 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 demonstrate that S-PRESSO outperforms both continuous and discrete baselines in audio quality, acoustic similarity and reconstruction metrics.
Executive Summary
The article introduces S-PRESSO, a novel neural audio compression model designed to achieve ultra-low bitrates for sound effect compression. Utilizing a pretrained latent diffusion model and offline quantization, S-PRESSO generates both continuous and discrete embeddings at bitrates as low as 0.096 kbps, with frame rates down to 1Hz. The model demonstrates superior performance in audio quality, acoustic similarity, and reconstruction metrics compared to existing continuous and discrete baselines, despite operating at high compression rates. This advancement pushes the boundaries of audio compression technology, offering significant implications for industries requiring efficient audio storage and transmission.
Key Points
- ▸ S-PRESSO achieves ultra-low bitrates down to 0.096 kbps.
- ▸ The model uses a pretrained latent diffusion model for decoding compressed audio embeddings.
- ▸ Offline quantization is employed to enhance compression efficiency.
- ▸ S-PRESSO outperforms existing baselines in audio quality and reconstruction metrics.
Merits
Innovative Approach
S-PRESSO introduces a novel combination of latent diffusion models and offline quantization, which significantly enhances compression efficiency and audio quality at ultra-low bitrates.
Superior Performance
The model demonstrates superior performance in audio quality, acoustic similarity, and reconstruction metrics compared to existing methods, making it a significant advancement in the field.
Versatility
S-PRESSO's ability to generate both continuous and discrete embeddings makes it versatile for various applications requiring different levels of audio fidelity.
Demerits
Trade-off in Fidelity
While S-PRESSO achieves high compression rates, it does so at the cost of exact fidelity, which may limit its use in applications requiring precise audio reproduction.
Computational Complexity
The use of a pretrained latent diffusion model may introduce computational complexity, which could be a limitation for real-time applications or devices with limited processing power.
Specialized Application
The model is specifically designed for sound effect compression, which may limit its applicability to other types of audio data.
Expert Commentary
The introduction of S-PRESSO represents a significant leap forward in the field of audio compression, particularly in the realm of ultra-low bitrate applications. By leveraging a pretrained latent diffusion model and offline quantization, the model achieves remarkable compression rates while maintaining high audio quality. This innovation is particularly noteworthy given the persistent challenges in achieving both high compression rates and audio fidelity. The model's ability to generate both continuous and discrete embeddings further enhances its versatility, making it suitable for a wide range of applications. However, the trade-off in exact fidelity and the potential computational complexity of the model are important considerations that may limit its applicability in certain contexts. From a policy perspective, the advancements in audio compression technology highlighted by S-PRESSO could influence data storage and transmission standards, as well as raise new regulatory considerations related to data privacy and intellectual property. Overall, S-PRESSO sets a new benchmark in audio compression and paves the way for future research in this critical area.
Recommendations
- ✓ Further research should explore the potential of S-PRESSO in real-time applications and its adaptability to different types of audio data beyond sound effects.
- ✓ Investigation into the computational efficiency of the model and potential optimizations for deployment on devices with limited processing power is recommended.
- ✓ Policy makers should consider the implications of advanced audio compression technologies on data storage and transmission standards, as well as address potential regulatory challenges related to data privacy and intellectual property.
