90 citations · 153 across the 7 of their papers we have counts for
10 papers
Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach
Karyna Isaieva, Yves Laprie, Nicolas Turpault +3
Tongue contour extraction from real-time magnetic resonance images is a nontrivial task due to the presence of artifacts manifesting in form of blurring or ghostly contours. In thi…
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
Francesca Ronchini, Samuele Cornell, Romain Serizel +3
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 a…
The impact of non-target events in synthetic soundscapes for sound event detection
Francesca Ronchini, Romain Serizel, Nicolas Turpault +1
Detection and Classification Acoustic Scene and Events Challenge 2021 Task 4 uses a heterogeneous dataset that includes both recorded and synthetic soundscapes. Until recently only…
What's All the FUSS About Free Universal Sound Separation Data?
Scott Wisdom, Hakan Erdogan, Daniel Ellis +6
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 typ…
Sound Event Detection and Separation: a Benchmark on Desed Synthetic Soundscapes
Nicolas Turpault, Romain Serizel, Scott Wisdom +5
We propose a benchmark of state-of-the-art sound event detection systems (SED). We designed synthetic evaluation sets to focus on specific sound event detection challenges. We anal…
Improving Sound Event Detection Metrics: Insights from DCASE 2020
Giacomo Ferroni, Nicolas Turpault, Juan Azcarreta +4
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. This paper compares convent…
Improving Sound Event Detection In Domestic Environments Using Sound Separation
Nicolas Turpault, Scott Wisdom, Hakan Erdogan +5
Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or nois…
Training Sound Event Detection On A Heterogeneous Dataset
Nicolas Turpault, Romain Serizel
Training a sound event detection algorithm on a heterogeneous dataset including both recorded and synthetic soundscapes that can have various labeling granularity is a non-trivial…
Limitations of weak labels for embedding and tagging
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.Ye…
Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments
Romain Serizel, Nicolas Turpault, Hamid Eghbal-Zadeh +1
This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of t…