F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation
Continuous audio autoencoders reconstruct waveforms well but often produce latents with weak structure for understanding, while self-supervised audio encoders capture semantics but are not directly decodable. This mismatch complicates a single audio tokenizer that must support both understanding and generation. We adapt continuous autoencoder latents to this setting with two components: a noise-regularized autoencoder bottleneck and a latent-side representation encoder. The bottleneck uses channel normalization and stochastic perturbation instead of KL-based variational training, yielding scale-controlled continuous latents for reconstruction and autoregressive generation. The representat...
| Authors | Dinghao Zhou, Xingchen Song, Di Wu et al. |
|---|---|
| Primary category | cs.SD |
| Published | 4 June 2026 |
| Last updated at source | 4 June 2026 |
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