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 101125 of 194 papers

TitleStatusHype
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
Improving Weakly Supervised Sound Event Detection with Causal Intervention0
Incremental Learning Algorithm for Sound Event Detection0
Interactive Dual-Conformer with Scene-Inspired Mask for Soft Sound Event Detection0
Learning to Detect Novel and Fine-Grained Acoustic Sequences Using Pretrained Audio Representations0
Leveraging Audio-Tagging Assisted Sound Event Detection using Weakified Strong Labels and Frequency Dynamic Convolutions0
Lightweight Sound Event Detection Model with RepVGG Architecture0
Mixstyle based Domain Generalization for Sound Event Detection with Heterogeneous Training Data0
Multi-Branch Learning for Weakly-Labeled Sound Event Detection0
Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers0
Pseudo Strong Labels from Frame-Level Predictions for Weakly Supervised Sound Event Detection0
Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events0
RCRNN-based Sound Event Detection System with Specific Speech Resolution0
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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