SoloAudio: Target Sound Extraction with Language-oriented Audio
  Diffusion Transformer
Authors: name: Helin Wang, name: Jiarui Hai, name: Yen-Ju Lu, name: Karan Thakkar, name: Mounya Elhilali, name: Najim Dehak

In this paper, we introduce SoloAudio, a novel diffusion-based generative
model for target sound extraction (TSE). Our approach trains latent diffusion
models on audio, replacing the previous U-Net backbone with a skip-connected
Transformer that operates on latent features. SoloAudio supports both
audio-oriented and language-oriented TSE by utilizing a CLAP model as the
feature extractor for target sounds. Furthermore, SoloAudio leverages synthetic
audio generated by state-of-the-art text-to-audio models for training,
demonstrating strong generalization to out-of-domain data and unseen sound
events. We evaluate this approach on the FSD Kaggle 2018 mixture dataset and
real data from AudioSet, where SoloAudio achieves the state-of-the-art results
on both in-domain and out-of-domain data, and exhibits impressive zero-shot and
few-shot capabilities. Source code and demos are released.