低资源音频编解码器（LRAC）：2025挑战赛说明
Low-Resource Audio Codec (LRAC): 2025 Challenge Description
AI总结：
针对神经音频编解码器在低资源、抗声学失真等边缘部署约束下的研究空白，该文推出2025低资源音频编解码器挑战赛，提供数据集、基线与评估框架，以推动资源受限场景下神经/混合编解码器的研发。
摘要：
尽管近年的神经音频编解码器在超低比特率下能提供优于传统方法的语音质量，但其实际应用仍受低资源运行、抗声学失真鲁棒性相关问题的阻碍。边缘部署场景要求编解码器在严格的计算约束下运行，同时保持低延迟与低比特率。背景噪声与混响的存在进一步要求设计方案能抵御这类性能退化。神经编解码器在这些约束下的性能表现，以及它们与语音增强的集成问题，目前仍基本未被研究。为推动该领域发展，我们推出2025低资源音频编解码器挑战赛，旨在面向资源受限应用开发神经型与混合型编解码器。组委会为参赛者提供标准化训练数据集、两套基线系统与一套综合评估框架。预计本次挑战赛将产出可应用于编解码器设计及相关下游音频任务的宝贵洞见。
英文摘要：
While recent neural audio codecs deliver superior speech quality at ultralow bitrates over traditional methods, their practical adoption is hindered by obstacles related to low-resource operation and robustness to acoustic distortions. Edge deployment scenarios demand codecs that operate under stringent compute constraints while maintaining low latency and bitrate. The presence of background noise and reverberation further necessitates designs that are resilient to such degradations. The performance of neural codecs under these constraints and their integration with speech enhancement remain largely unaddressed. To catalyze progress in this area, we introduce the 2025 Low-Resource Audio Codec Challenge, which targets the development of neural and hybrid codecs for resource-constrained applications. Participants are supported with a standardized training dataset, two baseline systems, and a comprehensive evaluation framework. The challenge is expected to yield valuable insights applicable to both codec design and related downstream audio tasks.
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。
