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Common target sound extraction (TSE) approaches primarily relied ondiscriminative approaches in order to separate the target sound whileminimizing interference from the unwanted sources, with varying success inseparating the target from the background. This study introduces DPM-TSE, afirst generative method based on diffusion probabilistic modeling (DPM) fortarget sound extraction, to achieve both cleaner target renderings as well asimproved separability from unwanted sounds. The technique also tackles commonbackground noise issues with DPM by introducing a correction method for noiseschedules and sample steps. This approach is evaluated using both objective andsubjective quality metrics on the FSD Kaggle 2018 dataset. The results showthat DPM-TSE has a significant improvement in perceived quality in terms oftarget extraction and purity.

DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction