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 81–90 of 194 papers

Title	Date	Tasks	Status	Score
A Closer Look at Weak Label Learning for Audio Events	Apr 24, 2018	Audio ClassificationEvent Detection	CodeCode Available	5
Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems	Oct 5, 2023	Event DetectionSound Event Detection	CodeCode Available	5
Learning Sound Events From Webly Labeled Data	Nov 25, 2018	Event DetectionSound Event Detection	CodeCode Available	5
Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures	May 27, 2021	Audio TaggingEvent Detection	CodeCode Available	5
Learning Sound Event Classifiers from Web Audio with Noisy Labels	Jan 4, 2019	General ClassificationSound Event Detection	CodeCode Available	5
Leveraging Geometrical Acoustic Simulations of Spatial Room Impulse Responses for Improved Sound Event Detection and Localization	Sep 6, 2023	Event DetectionSound Event Detection	CodeCode Available	5
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event Detection	Feb 28, 2025	Boundary DetectionEvent Detection	CodeCode Available	5
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection	Feb 21, 2017	Event DetectionSound Event Detection	CodeCode Available	5
Adaptive pooling operators for weakly labeled sound event detection	Apr 26, 2018	Event DetectionMultiple Instance Learning	CodeCode Available	5
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling	Jul 19, 2019	Event DetectionLanguage Modelling	CodeCode Available	5

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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