Papers › Exploring Text-Queried Sound Event Detection with Audio Source Separation

Exploring Text-Queried Sound Event Detection with Audio Source Separation

20 Sep 2024arXiv:2409.13292archive 2025-07-28

Han Yin, Jisheng Bai, Yang Xiao, Hui Wang, Siqi Zheng, Yafeng Chen, Rohan Kumar Das, Chong Deng, Jianfeng Chen

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.

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clip_mse apple-yinhan/tq-sed/models/sed.py official repository ran fingerprinted MIT (permissive) · 6dc7818c5777e187 · report
event_counts_and_durations apple-yinhan/tq-sed/sed_scores_eval/base_modules/ground_truth.py official repository ran MIT (permissive) · f68a9049b45e1f45 · report
fscore_curve_from_intermediate_statistics apple-yinhan/tq-sed/sed_scores_eval/base_modules/precision_recall.py official repository ran MIT (permissive) · df0a52d96e1396cc · report
fscore_from_precision_recall apple-yinhan/tq-sed/sed_scores_eval/base_modules/precision_recall.py official repository ran MIT (permissive) · c8a897b47e7b4f4d · report
multi_label_to_single_label_ground_truths apple-yinhan/tq-sed/sed_scores_eval/base_modules/ground_truth.py official repository ran MIT (permissive) · 3f017c80caed3382 · report
onset_deltas apple-yinhan/tq-sed/sed_scores_eval/base_modules/detection.py official repository ran fingerprinted MIT (permissive) · 8314f1aac70f95e9 · report
onset_offset_curves apple-yinhan/tq-sed/sed_scores_eval/base_modules/detection.py official repository ran MIT (permissive) · 437c3074d4fee848 · report
precision_recall_curve_from_intermediate_statistics apple-yinhan/tq-sed/sed_scores_eval/base_modules/precision_recall.py official repository ran MIT (permissive) · 8113a7700e87e50b · report

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Audio Source SeparationEvent DetectionSound Event Detection

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