arXiv AI
CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space
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arXiv AI
Jun 24
Beyond U-Net: A Latent-Representation-Aligned Skip-Free Backbone for Flow-Matching Speech Enhancement
arXiv:2606. 24745v1 Announce Type: cross Abstract: Generative models, particularly diffusion and score-based approaches, have recently achieved strong performance in speech enhancement, but their iterative sampling process limits real-time deployment.
arXiv AI
Oct 15
BridgeCodec: Mamba Enhanced Neural Audio Codec with Schr\"odinger Bridge at Low Bitrate
arXiv:2610.15453v1 Announce Type: cross Abstract: Neural audio codecs achieve remarkable performance but operate as closed ecosystems with rigidly co-trained encoderdecoder pairs, severely hindering...
arXiv AI
Jul 13
ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models
arXiv:2607. 09134v1 Announce Type: cross Abstract: Representation alignment (REPA) has been investigated to accelerate diffusion training, but we observe that regularizing intermediate representations in diffusion Transformers (DiT) may implicitly entangle latents and limit generative capacity.
arXiv AI
Jun 26
Latent-Mark: An Audio Watermark Robust to Neural Codec Compression
arXiv:2603. 05310v3 Announce Type: replace-cross Abstract: While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP) attacks, they remain vulnerable to neural compression.
arXiv Machine Learning
Sep 11
PitchFlower: A flow-based neural audio codec with pitch controllability
PitchFlower is a flow‑based neural audio codec that offers explicit pitch controllability by flattening and randomly shifting F0 contours during training while conditioning on the true F0 to reconstruct the original audio. A vector‑quantization bottleneck blocks pitch recovery, and a flow‑based decoder produces high‑quality audio. Experiments demonstrate that PitchFlower matches DSP baselines in pitch accuracy, surpasses state‑of‑the‑art neural codecs in audio quality, and remains robust even when trained on WORLD‑transformed audio, effectively removing vocoder artifacts.
arXiv AI
Jun 8
HybridCodec: Fast Dual-Stream, Semantically Enhanced Neural Audio Codec
arXiv:2606. 06743v1 Announce Type: cross Abstract: The popularity of neural audio codecs as speech tokenizers has surged with the advent of Multimodal Large Language Models.