arXiv:2606.06357v1 Announce Type: cross Abstract: 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 inste
The paper addresses a current challenge in AI concerning the unification of audio understanding and generation, which is critical for developing more versatile AI models.
This development proposes a method to create a single audio tokenizer for both understanding and generation, which could significantly streamline AI model development and improve performance in complex audio tasks.
The ability to produce structured, decodable latents from continuous audio autoencoders enables a new approach to building unified audio AI systems.
- · AI researchers
- · Audio software developers
- · Creative industries
- · Developers of fragmented audio AI solutions
Improved performance and efficiency in AI models for audio processing, synthesis, and analysis.
Accelerated development of advanced audio applications across various sectors, from voice assistants to music production.
Enhanced human-computer interaction through more natural and intelligent audio interfaces.
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Read at arXiv cs.AI