Sound event detection in synthetic audio: Analysis of the dcase 2016 task results

By Grégoire Lafay, Emmanouil Benetos, Mathieu Lagrange. 2017

23 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “In early DCASE Challenges, datasets recorded with professional equipment dominated, some of them being recorded with one single microphone model [13, 23, 80, 81].”
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks (2018, methodology): “The dataset is generated using the 11 isolated sound event classes from the DCASE 2016 task 2 dataset [58] such as speech, coughing,”
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge (2018, methodology): “A detailed statistical analysis of results for Task 2 is provided in [56].”