# Source separation with weakly labelled data: An approach to computational auditory scene analysis

arXiv: https://arxiv.org/abs/2002.02065 · HTML: https://mlsanity.com/paper/2002.02065/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Separate What You Describe: Language-Queried Audio Source Separation](https://mlsanity.com/paper/2203.15147/) (2022)
- [Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data](https://mlsanity.com/paper/2112.07891/) (2021)
- [CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos](https://mlsanity.com/paper/2212.07065/) (2022)
- [Real-Time Target Sound Extraction](https://mlsanity.com/paper/2211.02250/) (2022)
- [DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction](https://mlsanity.com/paper/2310.04567/) (2023)
- [ConceptBeam: Concept Driven Target Speech Extraction](https://mlsanity.com/paper/2207.11964/) (2022)
- [OmniSep: Unified Omni-Modality Sound Separation with Query-Mixup](https://mlsanity.com/paper/2410.21269/) (2024)
- [Environmental Sound Extraction Using Onomatopoeic Words](https://mlsanity.com/paper/2112.00209/) (2021)
- [Improving Generalization of Speech Separation in Real-World Scenarios: Strategies in Simulation, Optimization, and Evaluation](https://mlsanity.com/paper/2408.16126/) (2024)
- [Unleashing the Power of Natural Audio Featuring Multiple Sound Sources](https://mlsanity.com/paper/2504.17782/) (2025)
- [CATSE: A Context-Aware Framework for Causal Target Sound Extraction](https://mlsanity.com/paper/2403.14246/) (2024)
- [A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation](https://mlsanity.com/paper/2602.21476/) (2026)

## Abstract (arXiv)

Source separation is the task to separate an audio recording into individual sound sources. Source separation is fundamental for computational auditory scene analysis. Previous work on source separation has focused on separating particular sound classes such as speech and music. Many of previous work require mixture and clean source pairs for training. In this work, we propose a source separation framework trained with weakly labelled data. Weakly labelled data only contains the tags of an audio clip, without the occurrence time of sound events. We first train a sound event detection system with AudioSet. The trained sound event detection system is used to detect segments that are mostly like to contain a target sound event. Then a regression is learnt from a mixture of two randomly selected segments to a target segment conditioned on the audio tagging prediction of the target segment. Our proposed system can separate 527 kinds of sound classes from AudioSet within a single system. A U-Net is adopted for the separation system and achieves an average SDR of 5.67 dB over 527 sound classes in AudioSet.