PoDAR: Power-Decoupled Audio Representation for Generative Modeling

arXiv:2605.10084 · eess.AS, cs.AI, cs.LG, cs.SD · Submitted 2026-05-11 · Read on arXiv

eess.AS, cs.AI, cs.LG, cs.SD

Submitted: 2026-05-11

Updated: 2026-09-26

Code: https://github.com/Stability-AI/stable-audio-tools

Terminology

• Latent Space — 16× in this paper · explained in 7 episodes
• Fidelity — 11× in this paper · explained in 2 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Generative Modeling — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Latent Diffusion Model — 5× in this paper · explained in 2 episodes
• Latent Diffusion Models — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Classifier-Free Guidance — 3× in this paper
• Flow Matching — 3× in this paper · explained in 8 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Decoupling — 2× in this paper · explained in 2 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Centre for Speech Technology Research — 1× in this paper
• Computational overhead — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
• Diffusion Transformers with Representation Autoencoders
• Moshi: a speech-text foundation model for real-time dialogue
• DualCodec: A Low-Frame-Rate, Semantically-Enhanced Neural Audio Codec for Speech Generation
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
• What matters for Representation Alignment: Global Information or Spatial Structure?
• Seamless: Multilingual Expressive and Streaming Speech Translation

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• ProPS: Prompted Profile Synthesis for Natural Language-Conditioned Speaker Embedding Distributions