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

No papers found.

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