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 126150 of 194 papers

TitleStatusHype
DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels0
Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms0
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection0
DiffSED: Sound Event Detection with Denoising Diffusion0
Divided spectro-temporal attention for sound event localization and detection in real scenes for DCASE2023 challenge0
Do sound event representations generalize to other audio tasks? A case study in audio transfer learning0
Dual Knowledge Distillation for Efficient Sound Event Detection0
Effect of noise suppression losses on speech distortion and ASR performance0
End-to-End Polyphonic Sound Event Detection Using Convolutional Recurrent Neural Networks with Learned Time-Frequency Representation Input0
Energy Consumption Trends in Sound Event Detection Systems0
Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall0
Exploring the Potential of SSL Models for Sound Event Detection0
Fine-Grained Engine Fault Sound Event Detection Using Multimodal Signals0
FMSG-JLESS Submission for DCASE 2024 Task4 on Sound Event Detection with Heterogeneous Training Dataset and Potentially Missing Labels0
Formula-Supervised Sound Event Detection: Pre-Training Without Real Data0
Framework for evaluation of sound event detection in web videos0
Frequency Dynamic Convolutions for Sound Event Detection0
From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems0
Leveraging Language Model Capabilities for Sound Event Detection0
HiSSNet: Sound Event Detection and Speaker Identification via Hierarchical Prototypical Networks for Low-Resource Headphones0
Impact of Noisy Labels on Sound Event Detection: Deletion Errors Are More Detrimental Than Insertion Errors0
Impact of temporal resolution on convolutional recurrent networks for audio tagging and sound event detection0
Impact of visual assistance for automated audio captioning0
Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sørensen-Dice Coefficient Loss and Transfer Learning0
Improving Sound Event Detection Metrics: Insights from DCASE 20200
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ATST-SEDevent-based F1 score63.4Unverified
2SE-CRNN-16 with DualKDevent-based F1 score55.6Unverified
3FDY-CRNNevent-based F1 score54Unverified
4HTS-ATevent-based F1 score50.7Unverified
5RCTevent-based F1 score49.62Unverified
6FiltAug SEDevent-based F1 score49.6Unverified
7SED-SSep baseline dcase task 4 2020 v2event-based F1 score40.7Unverified
8Baseline dcase task 4 2020 v2event-based F1 score39Unverified
9Baselineevent-based F1 score25.8Unverified
10MAT-SEDPSDS10.59Unverified
#ModelMetricClaimedVerifiedStatus
1PHC SEDnet n=8Error Rate0.56Unverified
2Quaternion SEDnetError Rate0.52Unverified
3PHC SEDnet n=16Error Rate0.51Unverified
4PHC SEDnet n=4Error Rate0.45Unverified
5PHC SEDnet n=2Error Rate0.39Unverified
#ModelMetricClaimedVerifiedStatus
1CRNN (with BEATs + Separation)PSDS1 (-5dB)0.13Unverified
2CRNN (with BEATs)PSDS1 (-5dB)0.07Unverified
3CRNN (WildDESED + Curriculrm learning)PSDS1 (-5dB)0.05Unverified
4CRNN (WildDESED)PSDS1 (-5dB)0.05Unverified
5CRNNPSDS1 (-5dB)0.02Unverified
#ModelMetricClaimedVerifiedStatus
1DENetRank-1 Recognition Rate0.98Unverified
#ModelMetricClaimedVerifiedStatus
1DENetRank-1 Recognition Rate1Unverified