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 151–160 of 194 papers

Title	Date	Tasks	Status
Sound Event Detection in Domestic Environments using Dense Recurrent Neural Network	Nov 4, 2020	Data AugmentationEvent Detection	—Unverified
Improving Sound Event Detection Metrics: Insights from DCASE 2020	Oct 26, 2020	Event DetectionSound Event Detection	—Unverified
Power pooling: An adaptive pooling function for weakly labelled sound event detection	Oct 20, 2020	Event DetectionMultiple Instance Learning	—Unverified
Sound event detection via dilated convolutional recurrent neural networks	Jul 20, 2020	Event DetectionSound Event Detection	—Unverified
Conditioned Time-Dilated Convolutions for Sound Event Detection	Jul 10, 2020	Event DetectionLanguage Modelling	—Unverified
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture	Jul 2, 2020	Acoustic Scene ClassificationEvent Detection	—Unverified
Memory Controlled Sequential Self Attention for Sound Recognition	May 13, 2020	Event DetectionSound Event Detection	CodeCode Available
Incremental Learning Algorithm for Sound Event Detection	Mar 26, 2020	Event DetectionIncremental Learning	—Unverified
Multi-Branch Learning for Weakly-Labeled Sound Event Detection	Feb 22, 2020	Audio TaggingBoundary Detection	—Unverified
A Comparative Study of Western and Chinese Classical Music based on Soundscape Models	Feb 20, 2020	Emotion RecognitionEvent 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