AI 中文总结
本文通过测量四种神经音频编解码器与传统编解码器在笔记本和手机平台上的速率-能量-质量关系，发现低比特率并不保证能量优势，部署效率取决于解码器复杂度、运行时和传输能量系数等因素。
AI 中文摘要
神经音频编解码器可以在比传统编解码器更低的比特率下达到相似的主观质量，但其解码端的计算成本可能会在电池供电的客户端设备上抵消这一比特率优势。本文对四种神经音频编解码器（EnCodec、DAC、HILCodec和SNAC）以及两种传统基线（AAC-LC和Opus）在笔记本电脑和手机平台上进行了基于测量的速率-能量-质量分析。语音和音乐分别使用原始参考ViSQOL协议进行评估。对于主要比较，通过选择最接近处理级别平均ViSQOL共同重叠中点的测量点来匹配操作点。次要分析仅包括明确评估的比特率设置。解码器能量被测量为每秒音频的空闲扣除能量（J/s），结果以执行有效运行时和设备路径的三次重复运行的中位数进行总结。然后根据测量的解码器能量和比特率解析推导出成对的盈亏平衡传输能量阈值。在匹配质量的操作点上，所评估的神经编解码器在较低比特率下实现了相似的ViSQOL分数，但通常需要比适用的传统编解码器更多的解码器侧能量。EnCodec在两个平台上的匹配质量队列中均产生了最低的神经盈亏平衡阈值。相比之下，Phone XNNPACK CPU路径上的几个DAC和SNAC比较超过了200 mJ/kbit，其全频带Phone配置需要超过1秒才能解码1秒的音频。这些结果表明，仅降低比特率并不能保证能量优势：部署效率还取决于解码器复杂度、有效运行时和设备映射、执行有效性、加速器可用性以及传输能量系数。
英文摘要
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.
Comments11 pages, 14 figures, Submitted to IEEE Transactions on Consumer Electronics
