SOTAVerified

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

Showing 76–100 of 194 papers

TitleStatusHype
Sound event detection in domestic environments withweakly labeled data and soundscape synthesisCode0
City classification from multiple real-world sound scenesCode0
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive LearningCode0
Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural NetworkCode0
Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural NetworksCode0
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event DetectionCode0
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled SamplingCode0
Temporal Attention Pooling for Frequency Dynamic Convolution in Sound Event DetectionCode0
Learning Sound Event Classifiers from Web Audio with Noisy LabelsCode0
The Sounds of Home: A Speech-Removed Residential Audio Dataset for Sound Event DetectionCode0
Learning Sound Events From Webly Labeled DataCode0
Convolutional Recurrent Neural Networks for Polyphonic Sound Event DetectionCode0
Ubicoustics: Plug-and-Play Acoustic Activity RecognitionCode0
Leveraging Geometrical Acoustic Simulations of Spatial Room Impulse Responses for Improved Sound Event Detection and LocalizationCode0
Unsupervised Audio-Caption Aligning Learns Correspondences between Individual Sound Events and Textual PhrasesCode0
Cross-Referencing Self-Training Network for Sound Event Detection in Audio MixturesCode0
Guided learning for weakly-labeled semi-supervised sound event detectionCode0
The NIGENS General Sound Events Database—0
tinyCLAP: Distilling Constrastive Language-Audio Pretrained Models—0
Towards Understanding of Frequency Dependence on Sound Event Detection—0
Training sound event detection with soft labels from crowdsourced annotations—0
UCIL: An Unsupervised Class Incremental Learning Approach for Sound Event Detection—0
Uncertainty quantification for multiclass data description—0
Unified Audio Event Detection—0
USM-SED - A Dataset for Polyphonic Sound Event Detection in Urban Sound Monitoring Scenarios—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ATST-SEDevent-based F1 score63.4—Unverified
2SE-CRNN-16 with DualKDevent-based F1 score55.6—Unverified
3FDY-CRNNevent-based F1 score54—Unverified
4HTS-ATevent-based F1 score50.7—Unverified
5RCTevent-based F1 score49.62—Unverified
6FiltAug SEDevent-based F1 score49.6—Unverified
7SED-SSep baseline dcase task 4 2020 v2event-based F1 score40.7—Unverified
8Baseline dcase task 4 2020 v2event-based F1 score39—Unverified
9Baselineevent-based F1 score25.8—Unverified
10MAT-SEDPSDS10.59—Unverified
#ModelMetricClaimedVerifiedStatus
1PHC SEDnet n=8Error Rate0.56—Unverified
2Quaternion SEDnetError Rate0.52—Unverified
3PHC SEDnet n=16Error Rate0.51—Unverified
4PHC SEDnet n=4Error Rate0.45—Unverified
5PHC SEDnet n=2Error Rate0.39—Unverified
#ModelMetricClaimedVerifiedStatus
1CRNN (with BEATs + Separation)PSDS1 (-5dB)0.13—Unverified
2CRNN (with BEATs)PSDS1 (-5dB)0.07—Unverified
3CRNN (WildDESED + Curriculrm learning)PSDS1 (-5dB)0.05—Unverified
4CRNN (WildDESED)PSDS1 (-5dB)0.05—Unverified
5CRNNPSDS1 (-5dB)0.02—Unverified
#ModelMetricClaimedVerifiedStatus
1DENetRank-1 Recognition Rate0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1DENetRank-1 Recognition Rate1—Unverified