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arXiv · 2603.02022

CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space

Bowen Zhang·Junchuan Zhao·Ian McLoughlin·Ye Wang·A S Madhukumar
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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 incur higher computational cost and have limited high-frequency fidelity. Neural audio codecs offer compact latent representations that better preserve acoustic detail, yet accurately recovering high-resolution latent information remains challenging due to representation mismatch. We present CodecFlow, a neural codec-based BWE framework that performs efficient speech reconstruction in a compact latent space. CodecFlow employs a voicing-aware conditional flow converter on continuous codec embeddings and a structure-constrained residual vector quantizer to improve latent alignment stability. Optimized end-to-end, CodecFlow achieves strong spectral fidelity and enhanced perceptual quality on 8 kHz to 16 kHz and 44.1 kHz speech BWE tasks.

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Bowen Zhang, Junchuan Zhao, Ian McLoughlin, Ye Wang, A S Madhukumar. 2026-03-03. CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space. https://arxiv.org/abs/2603.02022

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