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 61–70 of 194 papers

Title	Date	Tasks	Status
Channel-Spatial-Based Few-Shot Bird Sound Event Detection	Jun 18, 2023	Event DetectionFew-Shot Learning	—Unverified
Fine-Grained Engine Fault Sound Event Detection Using Multimodal Signals	Mar 16, 2024	Event DetectionFault Detection	—Unverified
Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection	May 24, 2022	Event DetectionFew-Shot Learning	—Unverified
Impact of Noisy Labels on Sound Event Detection: Deletion Errors Are More Detrimental Than Insertion Errors	Aug 27, 2024	Event DetectionSound Event Detection	—Unverified
Impact of temporal resolution on convolutional recurrent networks for audio tagging and sound event detection	Sep 26, 2022	Audio TaggingEvent Detection	—Unverified
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture	Jul 2, 2020	Acoustic Scene ClassificationEvent Detection	—Unverified
Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall	Sep 25, 2023	Event DetectionSound Event Detection	—Unverified
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning	Mar 4, 2025	Active LearningEvent Detection	—Unverified
Energy Consumption Trends in Sound Event Detection Systems	Sep 13, 2024	Event DetectionSound Event Detection	—Unverified
End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input	May 9, 2018	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