VCNAC：一种可变通道神经音频编解码器用于单声道、立体声和环绕声
VCNAC: A Variable-Channel Neural Audio Codec for Mono, Stereo, and Surround Sound
AI总结：
VCNAC是一种可变通道神经音频编解码器，通过单一参数化编码器和解码器实现多通道音频的高质量重建与灵活推理。
摘要：
我们提出了VCNAC，一种可变通道的神经音频编解码器。我们的方法采用单一的编码器和解码器参数化，能够原生支持不同的通道设置，从单声道语音到电影级5.1通道环绕音频。通道兼容性目标确保多通道内容在解码到更少通道时仍能保持感知质量。共享的表示使可以在单一组码本上训练生成语言模型，同时在推理时支持跨模态和通道配置的可扩展性。使用客观空间音频度量和主观听觉测试评估表明，我们的统一方法在单声道、立体声和环绕音频配置中均能保持高质量的重建质量。
英文摘要：
We present VCNAC, a variable channel neural audio codec. Our approach features a single encoder and decoder parametrization that enables native inference for different channel setups, from mono speech to cinematic 5.1 channel surround audio. Channel compatibility objectives ensure that multi-channel content maintains perceptual quality when decoded to fewer channels. The shared representation enables training of generative language models on a single set of codebooks while supporting inference-time scalability across modalities and channel configurations. Evaluation using objective spatial audio metrics and subjective listening tests demonstrates that our unified approach maintains high reconstruction quality across mono, stereo, and surround audio configurations.
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。
