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 1–10 of 194 papers

Title	Date	Tasks	Status	Hype
Frequency Dynamic Convolutions for Sound Event Detection	Jun 15, 2025	ARCEvent Detection	—Unverified	0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event Detection	May 27, 2025	Active LearningDiversity	CodeCode Available	0
Exploring the Potential of SSL Models for Sound Event Detection	May 17, 2025	Event DetectionModel Selection	—Unverified	0
Temporal Attention Pooling for Frequency Dynamic Convolution in Sound Event Detection	Apr 17, 2025	Event DetectionSound Event Detection	CodeCode Available	0
Formula-Supervised Sound Event Detection: Pre-Training Without Real Data	Apr 6, 2025	Event DetectionSound Event Detection	—Unverified	0
Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models	Mar 14, 2025	Audio TaggingEvent Detection	CodeCode Available	1
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning	Mar 4, 2025	Active LearningEvent Detection	—Unverified	0
Robust detection of overlapping bioacoustic sound events	Mar 4, 2025	Event DetectionGraph Matching	—Unverified	0
Synthetic data enables context-aware bioacoustic sound event detection	Mar 1, 2025	Event DetectionIn-Context Learning	—Unverified	0
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event Detection	Feb 28, 2025	Boundary DetectionEvent Detection	CodeCode Available	0

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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