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

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Showing 1–10 of 194 papers

TitleStatusHype
Frequency Dynamic Convolutions for Sound Event Detection—0
Hybrid Disagreement-Diversity Active Learning for Bioacoustic Sound Event DetectionCode0
Exploring the Potential of SSL Models for Sound Event Detection—0
Temporal Attention Pooling for Frequency Dynamic Convolution in Sound Event DetectionCode0
Formula-Supervised Sound Event Detection: Pre-Training Without Real Data—0
Exploring Performance-Complexity Trade-Offs in Sound Event Detection ModelsCode1
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Robust detection of overlapping bioacoustic sound events—0
Synthetic data enables context-aware bioacoustic sound event detection—0
JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event DetectionCode0
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All datasetsDESEDL3DAS21WildDESEDMivia Audio EventsMivia Road Events

Benchmark Results

#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