  Foley sound generation, the art of creating audio for multimedia, has
recently seen notable advancements through text-conditioned latent diffusion
models. These systems use multimodal text-audio representation models, such as
Contrastive Language-Audio Pretraining (CLAP), whose objective is to map
corresponding audio and text prompts into a joint embedding space. AudioLDM, a
text-to-audio model, was the winner of the DCASE2023 task 7 Foley sound
synthesis challenge. The winning system fine-tuned the model for specific audio
classes and applied a post-filtering method using CLAP similarity scores
between output audio and input text at inference time, requiring the generation
of extra samples, thus reducing data generation efficiency. We introduce a new
loss term to enhance Foley sound generation in AudioLDM without post-filtering.
This loss term uses a new module based on the CLAP mode-Latent CLAP encode-to
align the latent diffusion output with real audio in a shared CLAP embedding
space. Our experiments demonstrate that our method effectively reduces the
Frechet Audio Distance (FAD) score of the generated audio and eliminates the
need for post-filtering, thus enhancing generation efficiency.
