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arXiv · 2609.34524

Measurement-Based Bitrate-Energy-Quality Analysis of Neural Audio Codec Decoders on Laptop and Phone Platforms

Seunghyeon Shin·Seokjin Lee
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Abstract

Neural audio codecs can achieve similar objective quality at lower bitrates than conventional codecs, but their decoder-side computational cost may offset this bitrate advantage on battery-powered client devices. This paper presents a measurement-based rate--energy--quality analysis of four neural audio codecs--EnCodec, DAC, HILCodec, and SNAC--and two conventional baselines, AAC-LC and Opus, on laptop and phone platforms. Speech and music are evaluated separately using the original-reference ViSQOL protocol. For the main comparison, operating points are matched by selecting the measured point nearest to the midpoint of the common overlap in treatment-level mean ViSQOL. A secondary analysis includes only explicitly evaluated bitrate settings. Decoder energy is measured as idle-subtracted energy per second of audio (J/s), and results are summarized as the median of three repeated runs for execution-valid runtime and device paths. Pairwise break-even transmission-energy thresholds are then derived analytically from the measured decoder energy and bitrate. At the matched-quality operating points, the evaluated neural codecs achieved similar ViSQOL scores at lower bitrates but generally required more decoder-side energy than the applicable conventional codecs. EnCodec produced the lowest neural break-even thresholds in both matched-quality cohorts on both platforms. By contrast, several DAC and SNAC comparisons on the Phone XNNPACK CPU path exceeded 200 mJ/kbit, and their full-band Phone configurations required more than 1 s to decode 1 s of audio. These results show that lower bitrate alone does not guarantee an energy benefit: deployment efficiency also depends on decoder complexity, the effective runtime and device mapping, execution validity, accelerator availability, and the transmission-energy coefficient.

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BibTeXRIS

Seunghyeon Shin, Seokjin Lee. 2026-09-28. Measurement-Based Bitrate-Energy-Quality Analysis of Neural Audio Codec Decoders on Laptop and Phone Platforms. https://arxiv.org/abs/2609.34524

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