# Improving Target Sound Extraction with Timestamp Information

arXiv: https://arxiv.org/abs/2204.00821 · HTML: https://mlsanity.com/paper/2204.00821/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2022.

## Built on by (most cited first, 10 of 10)

- [Separate Anything You Describe](https://mlsanity.com/paper/2308.05037/) (2023)
- [FlowSep: Language-Queried Sound Separation with Rectified Flow Matching](https://mlsanity.com/paper/2409.07614/) (2024)
- [DPM-TSE: A Diffusion Probabilistic Model for Target Sound Extraction](https://mlsanity.com/paper/2310.04567/) (2023)
- [SoloAudio: Target Sound Extraction with Language-oriented Audio Diffusion Transformer](https://mlsanity.com/paper/2409.08425/) (2024)
- [SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline](https://mlsanity.com/paper/2505.19314/) (2025)
- [Multichannel-to-Multichannel Target Sound Extraction Using Direction and Timestamp Clues](https://mlsanity.com/paper/2409.12415/) (2024)
- [SAM Audio Judge: A Unified Multimodal Framework for Perceptual Evaluation of Audio Separation](https://mlsanity.com/paper/2601.19702/) (2026)
- [FlowSep 2: Self-Supervised Flow Matching for Language-Queried Audio Source Separation](https://mlsanity.com/paper/2608.22111/) (2026)
- [Text-Queried Audio Source Separation via Hierarchical Modeling](https://mlsanity.com/paper/2505.21025/) (2025)
- [Shared Representation Learning for Reference-Guided Targeted Sound Detection](https://mlsanity.com/paper/2603.17025/) (2026)

## Abstract (arXiv)

Target sound extraction (TSE) aims to extract the sound part of a target sound event class from a mixture audio with multiple sound events. The previous works mainly focus on the problems of weakly-labelled data, jointly learning and new classes, however, no one cares about the onset and offset times of the target sound event, which has been emphasized in the auditory scene analysis. In this paper, we study to utilize such timestamp information to help extract the target sound via a target sound detection network and a target-weighted time-frequency loss function. More specifically, we use the detection result of a target sound detection (TSD) network as the additional information to guide the learning of target sound extraction network. We also find that the result of TSE can further improve the performance of the TSD network, so that a mutual learning framework of the target sound detection and extraction is proposed. In addition, a target-weighted time-frequency loss function is designed to pay more attention to the temporal regions of the target sound during training. Experimental results on the synthesized data generated from the Freesound Datasets show that our proposed method can significantly improve the performance of TSE.