Elastic Time: Dynamic Frame Rate Bottlenecks for Neural Audio Coding

• URL: http://arxiv.org/abs/2606.27320v1
• Date: Thu, 25 Jun 2026 17:33:09 GMT
• Title: Elastic Time: Dynamic Frame Rate Bottlenecks for Neural Audio Coding
• Abstract summary: We introduce Elastic Time, a dynamic frame-rate bottleneck that converts fixed-frame-rate autoencoders to dynamic ones.<n>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.
• Score: 33.15700732641714
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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