Elastic Time: Dynamic Frame Rate Bottlenecks for Neural Audio Coding
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Computer Science > Sound
Title:Elastic Time: Dynamic Frame Rate Bottlenecks for Neural Audio Coding
Abstract:Neural audio autoencoders have become a core component of compression, feature extraction, and generation. However, while existing systems support variable bitrate, the vast majority of models still operate at a fixed latent frame-rate, allocating equal temporal budget to regions with very different information density, which can result in unnecessarily long sequences. We introduce Elastic Time, a dynamic frame-rate bottleneck that converts fixed-frame-rate autoencoders to dynamic ones. Our method learns a lightweight latent predictor used to decide which frames can be skipped and later reconstructed, enabling efficient greedy boundary selection at inference. Experiments show our method enables deployment-time rate control while improving efficiency-quality tradeoffs relative to baselines. Overall, we provide a flexible mechanism for adjusting temporal resolution in audio autoencoders, potentially facilitating more efficient downstream modeling for generation and long-context tasks.
| Comments: | Interspeech 2026 |
| Subjects: | Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2606.27320 [cs.SD] |
| (or arXiv:2606.27320v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27320
arXiv-issued DOI via DataCite
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