Publications
ワークショップ (国際) Foley Sound Synthesis with a Class-conditioned Latent Diffusion Model
Robin Scheibler, Takuya Hasumi, Yusuke Fujita, Tatsuya Komatsu, Ryuichi Yamamoto, Kentaro Tachibana
Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
2023.9.20
We propose a competitive Foley sound synthesis system based on available components and fine-tuned on a target dataset. We reuse a text-to-audio pre-trained model composed of a latent diffusion model (LDM), trained on AudioCaps, a variational auto-encoder (VAE), and a vocoder. We fine-tune the LDM on the development dataset of the DCASE 2023 Task 7 to output a latent representation conditioned on the target class number. The VAE and vocoder are then used to generate the waveform from the latent representation. To improve the quality of the generated samples, we utilize a post-processing filter that selects a subset of generated sounds to match a distribution of target class sounds. In experiments, we found that our system achieved an average Fr?chet audio distance (FAD) of 4.744, which is significantly better than 9.702 produced by the baseline system of the DCASE 2023 Challenge Task 7. In addition, we perform ablation studies to evaluate the performance of the system before fine-tuning and the effect of sampling rate on the FAD.
Paper :
Foley Sound Synthesis with a Class-conditioned Latent Diffusion Model
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