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

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Showing 131–140 of 194 papers

Title	Date	Tasks	Status
Unsupervised Audio-Caption Aligning Learns Correspondences between Individual Sound Events and Textual Phrases	Oct 6, 2021	Event DetectionRetrieval	CodeCode Available
RCRNN-based Sound Event Detection System with Specific Speech Resolution	Oct 1, 2021	Data AugmentationEvent Detection	—Unverified
Uncertainty quantification for multiclass data description	Aug 29, 2021	ClassificationEvent Detection	—Unverified
Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers	Jul 28, 2021	Event DetectionKeyword Spotting	—Unverified
Crowdsourcing strong labels for sound event detection	Jul 26, 2021	Event DetectionSound Event Detection	—Unverified
Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sørensen-Dice Coefficient Loss and Transfer Learning	Jul 22, 2021	Data AugmentationEvent Detection	—Unverified
Sound Event Detection: A Tutorial	Jul 12, 2021	BIG-bench Machine LearningEvent Detection	CodeCode Available
Self-training with noisy student model and semi-supervised loss function for dcase 2021 challenge task 4	Jul 6, 2021	Event DetectionSound Event Detection	—Unverified
Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings	Jun 30, 2021	Event DetectionSound Event Detection	—Unverified
Do sound event representations generalize to other audio tasks? A case study in audio transfer learning	Jun 21, 2021	Event DetectionSound Event 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