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 171–180 of 194 papers

Title	Date	Tasks	Status
Evaluation of post-processing algorithms for polyphonic sound event detection	Jun 17, 2019	Audio TaggingEvent Detection	CodeCode Available
Guided learning for weakly-labeled semi-supervised sound event detection	Jun 6, 2019	Audio TaggingBoundary Detection	CodeCode Available
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection	May 24, 2019	Event DetectionMulti-Label Classification	CodeCode Available
Robust sound event detection in bioacoustic sensor networks	May 20, 2019	Data AugmentationEvent Detection	CodeCode Available
The NIGENS General Sound Events Database	Feb 21, 2019	Event DetectionSound Event Detection	—Unverified
Learning Sound Event Classifiers from Web Audio with Noisy Labels	Jan 4, 2019	General ClassificationSound Event Detection	CodeCode Available
Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events	Dec 17, 2018	Event DetectionSound Event Detection	—Unverified
Learning Sound Events From Webly Labeled Data	Nov 25, 2018	Event DetectionSound Event Detection	CodeCode Available
DASED: A Multi-Domain Dataset for Sound Event Detection Domain Adaptation	Oct 19, 2018	Domain AdaptationEvent Detection	—Unverified
Polyphonic Sound Event Detection by using Capsule Neural Network	Oct 15, 2018	Event DetectionSound Event Detection	CodeCode Available

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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