  This paper introduces a novel task in generative speech processing, Acoustic
Scene Transfer (AST), which aims to transfer acoustic scenes of speech signals
to diverse environments. AST promises an immersive experience in speech
perception by adapting the acoustic scene behind speech signals to desired
environments. We propose AST-LDM for the AST task, which generates speech
signals accompanied by the target acoustic scene of the reference prompt.
Specifically, AST-LDM is a latent diffusion model conditioned by CLAP
embeddings that describe target acoustic scenes in either audio or text
modalities. The contributions of this paper include introducing the AST task
and implementing its baseline model. For AST-LDM, we emphasize its core
framework, which is to preserve the input speech and generate audio
consistently with both the given speech and the target acoustic environment.
Experiments, including objective and subjective tests, validate the feasibility
and efficacy of our approach.
