Latent Diffusion Model Based Foley Sound Generation System For DCASE Challenge 2023 Task 7

Foley sound generation aims to synthesise the background sound for multimedia content, which involves computationally modelling sound effects with specialized techniques. In this work, we proposed a diffusion based generative model for DCASE 2023 challenge task 7: Foley Sound Synthesis. The proposed system is based on AudioLDM, which is a diffusion-based text-to-audio generation model. To alleviate the data scarcity of the task 7 training set, our model is initially trained with large-scale datasets and downstream into this DCASE task via transfer learning. We have observed that the feature extracted by the encoder can significantly affect the performance of the generation model. Hence, we improve the results by leveraging the input label with related text embedding features obtained by a large language model, i.e., contrastive language-audio pretraining (CLAP). In addition, we utilize a filtering strategy to further refine the output, i.e. by selecting the best results from the candidate clips generated in terms of the similarity score between the sound and target labels. The overall system achieves a Frechet audio distance (FAD) score of 4.765 on average among all seven different classes, substantially outperforming the baseline system which achieves a FAD score of 9.7.