Acoustic Teleportation Via Disentangled Neural Audio Codec Representations

This paper presents an approach for acoustic teleportation by disentangling speech content from acoustic environment characteristics in neural audio codec representations. Acoustic teleportation transfers room characteristics between speech recordings while preserving content and speaker identity, enabling applications in telecommunications and virtual acoustic environments. Extending prior work by adopting the EnCodec architecture, we achieve substantial objective quality improvements with non-intrusive ScoreQ scores of 3.03, compared to 2.44 for previous methods. Our training strategy incorporates five tasks: clean reconstruction, reverberated reconstruction, dereverberation, and two variants of acoustic teleportation. We analyze the trade-off between temporal downsampling of acoustic embeddings and reconstruction quality, demonstrating that even a downsampling factor of two yields a statistically significant degradation in reconstruction quality. The learned acoustic embeddings exhibit a strong correlation with reverberation time. t-SNE analysis reveals that acoustic embeddings cluster predominantly by room while speech embeddings cluster by speaker, confirming substantial disentanglement sufficient for effective acoustic teleportation.