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.

翻译：尽管近期神经音频编解码器在超低比特率下相比传统方法实现了更优越的语音质量，但其实际应用仍受限于低资源运行条件及对声学失真的鲁棒性等挑战。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能抵御此类信号劣化的方案。神经编解码器在此类约束下的性能及其与语音增强技术的融合，目前仍未得到充分研究。为推进该领域发展，我们推出2025年低资源音频编解码器挑战赛，旨在推动面向资源受限应用的神经与混合编解码器开发。参赛者将获得标准化训练数据集、两套基线系统及一套综合评估框架的支持。本挑战赛预期将为编解码器设计及相关下游音频任务提供有价值的见解。