# Sound Event Detection in Synthetic Audio: Analysis of the DCASE 2016 Task Results

arXiv: https://arxiv.org/abs/1711.05551 · HTML: https://mlsanity.com/paper/1711.05551/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers](https://mlsanity.com/paper/2211.13956/) (2022)
- [Automatic Detection Of Noise Events at Shooting Range Using Machine Learning](https://mlsanity.com/paper/2107.11453/) (2021)
- [Determining the origin of impulsive noise events using paired wireless sound sensors](https://mlsanity.com/paper/2108.11758/) (2021)
- [SsgCaps: A controlled dataset for the evaluation of sound scene generation algorithms](https://mlsanity.com/paper/2609.26854/) (2026)

## Abstract (arXiv)

As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013, studies the behaviour of tested algorithms when facing controlled levels of audio complexity with respect to background noise and polyphony/density, with the added benefit of a very accurate ground truth. This paper presents the task formulation, evaluation metrics, submitted systems, and provides a statistical analysis of the results achieved, with respect to various aspects of the evaluation dataset.