Sound Event Detection

Sound Event Detection (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is referred as polyphonic SED.

Source: A report on sound event detection with different binaural features

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 121–130 of 194 papers

Title	Date	Tasks	Status
Multi-Branch Learning for Weakly-Labeled Sound Event Detection	Feb 22, 2020	Audio TaggingBoundary Detection	—Unverified
Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers	Jul 28, 2021	Event DetectionKeyword Spotting	—Unverified
Pseudo Strong Labels from Frame-Level Predictions for Weakly Supervised Sound Event Detection	Jan 7, 2025	Event DetectionSound Event Detection	—Unverified
Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events	Dec 17, 2018	Event DetectionSound Event Detection	—Unverified
RCRNN-based Sound Event Detection System with Specific Speech Resolution	Oct 1, 2021	Data AugmentationEvent Detection	—Unverified
LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy	Feb 18, 2023	Event DetectionMissing Elements	—Unverified
Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings	Jun 30, 2021	Event DetectionSound Event Detection	—Unverified
Robust detection of overlapping bioacoustic sound events	Mar 4, 2025	Event DetectionGraph Matching	—Unverified
SEED: Sound Event Early Detection via Evidential Uncertainty	Feb 5, 2022	Event DetectionSound Event Detection	—Unverified
SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation	Aug 9, 2024	Computational EfficiencyEvent Detection	—Unverified

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All datasets DESED L3DAS21 WildDESED Mivia Audio Events Mivia Road Events

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ATST-SED	event-based F1 score	63.4	—	Unverified
2	SE-CRNN-16 with DualKD	event-based F1 score	55.6	—	Unverified
3	FDY-CRNN	event-based F1 score	54	—	Unverified
4	HTS-AT	event-based F1 score	50.7	—	Unverified
5	RCT	event-based F1 score	49.62	—	Unverified
6	FiltAug SED	event-based F1 score	49.6	—	Unverified
7	SED-SSep baseline dcase task 4 2020 v2	event-based F1 score	40.7	—	Unverified
8	Baseline dcase task 4 2020 v2	event-based F1 score	39	—	Unverified
9	Baseline	event-based F1 score	25.8	—	Unverified
10	MAT-SED	PSDS1	0.59	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PHC SEDnet n=8	Error Rate	0.56	—	Unverified
2	Quaternion SEDnet	Error Rate	0.52	—	Unverified
3	PHC SEDnet n=16	Error Rate	0.51	—	Unverified
4	PHC SEDnet n=4	Error Rate	0.45	—	Unverified
5	PHC SEDnet n=2	Error Rate	0.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CRNN (with BEATs + Separation)	PSDS1 (-5dB)	0.13	—	Unverified
2	CRNN (with BEATs)	PSDS1 (-5dB)	0.07	—	Unverified
3	CRNN (WildDESED + Curriculrm learning)	PSDS1 (-5dB)	0.05	—	Unverified
4	CRNN (WildDESED)	PSDS1 (-5dB)	0.05	—	Unverified
5	CRNN	PSDS1 (-5dB)	0.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DENet	Rank-1 Recognition Rate	0.98	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DENet	Rank-1 Recognition Rate	1	—	Unverified