English(EN) CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space

CodecFlow框架通过神经编解码器增强语音带宽扩展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了CodecFlow，一个在神经音频编解码器潜在空间中运行的用于语音带宽扩展的新型框架。该方法旨在通过比传统方法更有效、更保真地重建高频内容，来提高低带宽语音的清晰度和可懂度。CodecFlow利用一个感知感知条件流转换器和一个结构约束残差向量量化器来增强潜在对齐稳定性，在8 kHz到16 kHz以及44.1 kHz语音带宽扩展任务上均表现出强大的性能。 AI

影响该框架可能在电话会议和语音助手等应用中实现更高效、更高质量的语音处理。

排序理由该集群包含一篇在arXiv上发表的关于新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Bowen Zhang, Junchuan Zhao, Ian McLoughlin, Ye Wang, A S Madhukumar · 2026-10-15 04:00

CodecFlow：神经编解码器潜在空间中的条件流匹配实现高效带宽扩展

arXiv:2603.02022v3 Announce Type: replace-cross Abstract: Speech Bandwidth Extension improves clarity and intelligibility by restoring/inferring appropriate high-frequency content for low-bandwidth speech. Existing methods often rely on spectrogram or waveform modeling, which can…

本文如何被排名

报道来源 [1]

CodecFlow：神经编解码器潜在空间中的条件流匹配实现高效带宽扩展

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