Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system

no code implementations • 14 Oct 2022 • Francesca Ronchini, Samuele Cornell, Romain Serizel, Nicolas Turpault, Eduardo Fonseca, Daniel P. W. Ellis

The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset.

Event Segmentation

The impact of non-target events in synthetic soundscapes for sound event detection

1 code implementation • 28 Sep 2021 • Francesca Ronchini, Romain Serizel, Nicolas Turpault, Samuele Cornell

Detection and Classification Acoustic Scene and Events Challenge 2021 Task 4 uses a heterogeneous dataset that includes both recorded and synthetic soundscapes.

Event Detection Sound Event Detection

What's All the FUSS About Free Universal Sound Separation Data?

no code implementations • 2 Nov 2020 • Scott Wisdom, Hakan Erdogan, Daniel Ellis, Romain Serizel, Nicolas Turpault, Eduardo Fonseca, Justin Salamon, Prem Seetharaman, John Hershey

We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types.

Data Augmentation

Improving Sound Event Detection Metrics: Insights from DCASE 2020

no code implementations • 26 Oct 2020 • Giacomo Ferroni, Nicolas Turpault, Juan Azcarreta, Francesco Tuveri, Romain Serizel, Çagdaş Bilen, Sacha Krstulović

The ranking of sound event detection (SED) systems may be biased by assumptions inherent to evaluation criteria and to the choice of an operating point.

Event Detection Sound Event Detection +1

Limitations of weak labels for embedding and tagging

1 code implementation • 5 Feb 2020 • Nicolas Turpault, Romain Serizel, Emmanuel Vincent

Many datasets and approaches in ambient sound analysis use weakly labeled data. Weak labels are employed because annotating every data sample with a strong label is too expensive. Yet, their impact on the performance in comparison to strong labels remains unclear. Indeed, weak labels must often be dealt with at the same time as other challenges, namely multiple labels per sample, unbalanced classes and/or overlapping events. In this paper, we formulate a supervised learning problem which involves weak labels. We create a dataset that focuses on the difference between strong and weak labels as opposed to other challenges.

Sound event detection in domestic environments withweakly labeled data and soundscape synthesis

1 code implementation • 26 Oct 2019 • Nicolas Turpault, Romain Serizel, Ankit Shah, Justin Salamon

This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results.

Event Detection Sound Event Detection