聚焦低资源场景的神经音频编码器挑战，推动高效低延迟语音压缩技术发展。
Low-Resource Audio Codec (LRAC): 2025 Challenge Description
- 设计针对边缘设备的轻量级神经与混合编码方案
- 在严苛计算约束下实现低延迟、低比特率稳定运行
- 适合语音增强、物联网等资源受限应用研究者参与
尽管近年神经音频编码器在超低比特率下相比传统方法显著提升了语音质量，但其在实际部署中仍面临低资源环境运行与抗声学失真能力不足的问题。边缘部署场景要求编码器在严格计算限制下保持低延迟和低比特率。背景噪声与混响的存在更要求编码设计具备鲁棒性。当前神经编码器在这些约束下的性能及其与语音增强的集成尚未充分研究。为推动该领域进展，我们发起2025年低资源音频编码挑战，旨在开发适用于资源受限场景的神经与混合编码器。参赛者将获得标准化训练数据集、两个基线系统及全面评估框架。该挑战有望为编码器设计及相关下游音频任务提供重要洞见。
原文摘要 · Abstract (English)
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.
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