Codec-SUPERB：声音编解码器模型的深度分析
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models
AI总结：
本研究推出Codec-SUPERB声音编解码器通用性能基准生态系统，从应用和信号双视角评估模型，设在线排行榜促进协作，并将开源相关资源以推动领域发展。
摘要：
声音编解码器兼具降低数据传输延迟与充当分词器的双重作用，凸显了其关键地位。近年来编解码器模型取得了显著发展。理想的声音编解码器应能保留内容、副语言信息、说话人特征及音频信息。然而，哪种编解码器能实现最优的声音信息保留仍无定论，因为不同论文均在各自选择的实验设置下评估模型。本研究推出Codec-SUPERB（Codec sound processing Universal PERformance Benchmark，编解码器声音处理通用性能基准），这是一个旨在基于声音领域知识，在代表性声音应用和信号级指标下评估编解码器模型的生态系统。Codec-SUPERB通过在线排行榜简化结果共享，在社区驱动的基准数据库中促进协作，从而推动编解码器的新发展周期。此外，我们开展了深度分析，从应用和信号两个视角为编解码器模型提供洞见，区别于以往主要聚焦信号级比较的编解码器研究论文。最后，我们将发布代码、排行榜和数据，以加速社区内的研究进展。
英文摘要：
The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance. Recent years have witnessed significant developments in codec models. The ideal sound codec should preserve content, paralinguistics, speakers, and audio information. However, the question of which codec achieves optimal sound information preservation remains unanswered, as in different papers, models are evaluated on their selected experimental settings. This study introduces Codec-SUPERB, an acronym for Codec sound processing Universal PERformance Benchmark. It is an ecosystem designed to assess codec models across representative sound applications and signal-level metrics rooted in sound domain knowledge.Codec-SUPERB simplifies result sharing through an online leaderboard, promoting collaboration within a community-driven benchmark database, thereby stimulating new development cycles for codecs. Furthermore, we undertake an in-depth analysis to offer insights into codec models from both application and signal perspectives, diverging from previous codec papers mainly concentrating on signal-level comparisons. Finally, we will release codes, the leaderboard, and data to accelerate progress within the community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
