Threshold Independent Evaluation of Sound Event Detection Scores
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Abstract (arXiv)
Performing an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research. The recently proposed polyphonic sound detection (PSD)-receiver operating characteristic (ROC) and PSD score (PSDS) make an important step into the direction of an evaluation of SED systems which is independent from a certain decision threshold. This allows to obtain a more complete picture of the overall system behavior which is less biased by threshold tuning. Yet, the PSD-ROC is currently only approximated using a finite set of thresholds. The choice of the thresholds used in approximation, however, can have a severe impact on the resulting PSDS. In this paper we propose a method which allows for computing system performance on an evaluation set for all possible thresholds jointly, enabling accurate computation not only of the PSD-ROC and PSDS but also of other collar-based and intersection-based performance curves. It further allows to select the threshold which best fulfills the requirements of a given application. Source code is publicly available in our SED evaluation package sed_scores_eval.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Jan 2022.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 5; 2026: 2.
10 papers.
- 2025Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learningcited by 13
- 2025TACOS: Temporally-aligned Audio CaptiOnS for Language-Audio Pretrainingcited by 9
- 2023Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?cited by 4
- 2025FlexSED: Towards Open-Vocabulary Sound Event Detectioncited by 4
- 2025Revisiting Audio-language Pretraining for Learning General-purpose Audio Representationcited by 4
- 2023Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recallcited by 3
- 2026RealDESED: A Real-World Domestic Sound Event Detection Benchmarkcited by 1
- 2024MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detectioncited by 0
- 2025Exploring Performance-Complexity Trade-Offs in Sound Event Detection Modelscited by 0
- 2026Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Losscited by 0
- 2026RealDESED: A Real-World Domestic Sound Event Detection Benchmark18 Jul 2026
- 2026Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss29 Jun 2026
- 2025Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning22 Dec 2025
- 2025Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation20 Nov 2025
- 2025FlexSED: Towards Open-Vocabulary Sound Event Detection23 Sep 2025
- 2025TACOS: Temporally-aligned Audio CaptiOnS for Language-Audio Pretraining12 May 2025
- 2025Exploring Performance-Complexity Trade-Offs in Sound Event Detection Models14 Mar 2025
- 2024MTDA-HSED: Mutual-Assistance Tuning and Dual-Branch Aggregating for Heterogeneous Sound Event Detection10 Sep 2024
- 2023Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?27 Sep 2023
- 2023Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall25 Sep 2023