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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 194 papers

TitleStatusHype
Self Training and Ensembling Frequency Dependent Networks with Coarse Prediction Pooling and Sound Event Bounding BoxesCode1
Full-frequency dynamic convolution: a physical frequency-dependent convolution for sound event detectionCode1
Multi-Task Learning for Interpretable Weakly Labelled Sound Event DetectionCode1
Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event DetectionCode1
What Makes Sound Event Localization and Detection Difficult? Insights from Error AnalysisCode1
Exploring Performance-Complexity Trade-Offs in Sound Event Detection ModelsCode1
Conditioned Time-Dilated Convolutions for Sound Event Detection—0
Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms—0
A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds—0
Exploring the Potential of SSL Models for Sound Event Detection—0
Channel-Spatial-Based Few-Shot Bird Sound Event Detection—0
Fine-Grained Engine Fault Sound Event Detection Using Multimodal Signals—0
Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection—0
Improving Sound Event Detection Metrics: Insights from DCASE 2020—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Energy Consumption Trends in Sound Event Detection Systems—0
End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input—0
Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification—0
BAT: Learning to Reason about Spatial Sounds with Large Language Models—0
Effect of noise suppression losses on speech distortion and ASR performance—0
Affinity Mixup for Weakly Supervised Sound Event Detection—0
Dual Knowledge Distillation for Efficient Sound Event Detection—0
Active Learning for Sound Event Detection—0
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All datasetsDESEDL3DAS21WildDESEDMivia Audio EventsMivia Road Events

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