2023/10/06 by Jiarui Hai, Hai, Jiarui, Helin Wang +9 · 4 citations
Computer Science · Earth and Planetary Sciences · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #Underwater Acoustics Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.04567
openalex publication_date 2023/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Common target sound extraction (TSE) approaches primarily relied on discriminative approaches in order to separate the target sound while minimizing interference from the unwanted sources, with varying success in separating the target from the background. This study introduces DPM-TSE, a first generative method based on diffusion probabilistic modeling (DPM) for target sound extraction, to achieve both cleaner target renderings as well as improved separability from unwanted sounds. The technique also tackles common background noise issues with DPM by introducing a correction method for noise schedules and sample steps. This approach is evaluated using both objective and subjective quality metrics on the FSD Kaggle 2018 dataset. The results show that DPM-TSE has a significant improvement in perceived quality in terms of target extraction and purity.