# Exploring Text-Queried Sound Event Detection with Audio Source Separation

arXiv: https://arxiv.org/abs/2409.13292 · HTML: https://mlsanity.com/paper/2409.13292/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2024.

## Built on by (most cited first, 5 of 5)

- [MNV-17: A High-Quality Performative Mandarin Dataset for Nonverbal Vocalization Recognition in Speech](https://mlsanity.com/paper/2509.18196/) (2025)
- [FlexSED: Towards Open-Vocabulary Sound Event Detection](https://mlsanity.com/paper/2509.18606/) (2025)
- [MARS-Sep: Multimodal-Aligned Reinforced Sound Separation](https://mlsanity.com/paper/2510.10509/) (2025)
- [ASCMamba: Multimodal Time-Frequency Mamba for Acoustic Scene Classification](https://mlsanity.com/paper/2508.15632/) (2025)
- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)

## Abstract (arXiv)

In sound event detection (SED), overlapping sound events pose a significant challenge, as certain events can be easily masked by background noise or other events, resulting in poor detection performance. To address this issue, we propose the text-queried SED (TQ-SED) framework. Specifically, we first pre-train a language-queried audio source separation (LASS) model to separate the audio tracks corresponding to different events from the input audio. Then, multiple target SED branches are employed to detect individual events. AudioSep is a state-of-the-art LASS model, but has limitations in extracting dynamic audio information because of its pure convolutional structure for separation. To address this, we integrate a dual-path recurrent neural network block into the model. We refer to this structure as AudioSep-DP, which achieves the first place in DCASE 2024 Task 9 on language-queried audio source separation (objective single model track). Experimental results show that TQ-SED can significantly improve the SED performance, with an improvement of 7.22% on F1 score over the conventional framework. Additionally, we setup comprehensive experiments to explore the impact of model complexity. The source code and pre-trained model are released at https://github.com/apple-yinhan/TQ-SED.