LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

Problem

High frame rates in neural audio codecs result in long sequence lengths, leading to increased computational costs. This paper addresses the inefficiencies associated with existing dynamic frame rate methods, which struggle to balance rate and quality effectively. The work is presented as a preprint and has not undergone peer review.

Method

The proposed architecture, LACE (Layer-Adaptive Codec Encoding), implements independent compression at each quantization layer. This approach utilizes union alignment and boundary anchor mechanisms to ensure consistent durations across layers, which is critical for maintaining audio quality while optimizing computational efficiency. The experiments were conducted using the LibriTTS dataset, although the specific training compute resources utilized are not disclosed.

Results

LACE demonstrates a superior rate-quality tradeoff compared to prior dynamic frame rate methods, although the paper does not specify the baseline for comparison. Additionally, it shows improved TTS inference efficiency while maintaining competitive synthesis quality, again without naming the baseline for this comparison. The available text does not report quantitative results.

Limitations

The authors do not report any limitations in their work. However, the lack of specified baselines for comparison may hinder the ability to fully assess the performance improvements claimed.

Why it matters

The implications of this work are significant for downstream applications in neural audio synthesis, particularly in scenarios where computational resources are constrained. By improving the efficiency of dynamic frame rate codecs, LACE could facilitate the deployment of high-quality audio synthesis in real-time applications, potentially impacting areas such as virtual assistants, gaming, and interactive media.