The Low-Resource Audio Codec (LRAC) Challenge aims to advance neural audio coding for deployment in resource-constrained environments. The first edition focuses on low-resource neural speech codecs that must operate reliably under everyday noise and reverberation, while satisfying strict constraints on computational complexity, latency, and bitrate. Track 1 targets transparency codecs, which aim to preserve the perceptual transparency of input speech under mild noise and reverberation. Track 2 addresses enhancement codecs, which combine coding and compression with denoising and dereverberation. This paper presents the official baseline systems for both tracks in the 2025 LRAC Challenge. The baselines are convolutional neural codec models with Residual Vector Quantization, trained end-to-end using a combination of adversarial and reconstruction objectives. We detail the data filtering and augmentation strategies, model architectures, optimization procedures, and checkpoint selection criteria.

翻译：低资源音频编解码（LRAC）挑战赛旨在推动神经音频编码在资源受限环境中的部署应用。首届赛事聚焦于低资源神经语音编解码器，要求其在日常噪声和混响条件下可靠工作，同时满足计算复杂度、延迟和比特率的严格约束。赛道1针对透明编解码器，其目标是在轻度噪声和混响条件下保持输入语音的感知透明度。赛道2则关注增强编解码器，其将编码压缩与去噪去混响功能相结合。本文介绍了2025年LRAC挑战赛两个赛道的官方基线系统。这些基线系统为采用残差矢量量化的卷积神经编解码模型，通过结合对抗性与重建目标进行端到端训练。我们详细阐述了数据过滤与增强策略、模型架构、优化流程以及检查点选择标准。