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. We build upon previous work using the EnCodec architecture, achieving substantial objective quality improvements with non-intrusive ScoreQ scores of 3.03, compared to 2.44 for prior methods. Our training strategy incorporates five tasks: clean reconstruction, reverberated reconstruction, dereverberation, and two variants of acoustic teleportation. We demonstrate that temporal downsampling of the acoustic embedding significantly degrades performance, with even 2x downsampling resulting in a statistically significant reduction in quality. The learned acoustic embeddings exhibit strong correlations with RT60. Effective disentanglement is demonstrated using t-SNE clustering analysis, where acoustic embeddings cluster by room while speech embeddings cluster by speaker.