# Evaluation of post-processing algorithms for polyphonic sound event detection

arXiv: https://arxiv.org/abs/1906.06909 · HTML: https://mlsanity.com/paper/1906.06909/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection](https://mlsanity.com/paper/2110.02011/) (2021)
- [Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild](https://mlsanity.com/paper/2208.07522/) (2022)
- [Fast threshold optimization for multi-label audio tagging using Surrogate gradient learning](https://mlsanity.com/paper/2103.00833/) (2021)
- [Improving Post-Processing of Audio Event Detectors Using Reinforcement Learning](https://mlsanity.com/paper/2208.09201/) (2022)
- [WhaleVAD-BPN: Improving Baleen Whale Call Detection with Boundary Proposal Networks and Post-processing Optimisation](https://mlsanity.com/paper/2510.21280/) (2025)

## Abstract (arXiv)

Sound event detection (SED) aims at identifying audio events (audio tagging task) in recordings and then locating them temporally (localization task). This last task ends with the segmentation of the frame-level class predictions, that determines the onsets and offsets of the audio events. Yet, this step is often overlooked in scientific publications. In this paper, we focus on the post-processing algorithms used to identify the audio event boundaries. Different post-processing steps are investigated, through smoothing, thresholding, and optimization. In particular, we evaluate different approaches for temporal segmentation, namely statistic-based and parametric methods. Experiments are carried out on the DCASE 2018 challenge task 4 data. We compared post-processing algorithms on the temporal prediction curves of two models: one based on the challenge's baseline and a Multiple Instance Learning (MIL) model. Results show the crucial impact of the post-processing methods on the final detection score. Statistic-based methods yield a 22.9% event-based F-score on the evaluation set with our MIL model. Moreover, the best results were obtained using class-dependent parametric methods with 32.0% F-score.