90 citations · 153 across the 11 of their papers we have counts for
14 papers
Diffused Responsibility: Analyzing the Energy Consumption of Generative Text-to-Audio Diffusion Models
Riccardo Passoni, Francesca Ronchini, Luca Comanducci +2
Text-to-audio models have recently emerged as a powerful technology for generating sound from textual descriptions. However, their high computational demands raise concerns about e…
DCASE 2024 Task 4: Sound Event Detection with Heterogeneous Data and Missing Labels
Samuele Cornell, Janek Ebbers, Constance Douwes +4
The Detection and Classification of Acoustic Scenes and Events Challenge Task 4 aims to advance sound event detection (SED) systems in domestic environments by leveraging training…
Performance and energy balance: a comprehensive study of state-of-the-art sound event detection systems
Francesca Ronchini, Romain Serizel
In recent years, deep learning systems have shown a concerning trend toward increased complexity and higher energy consumption. As researchers in this domain and organizers of one…
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…
A benchmark of state-of-the-art sound event detection systems evaluated on synthetic soundscapes
Francesca Ronchini, Romain Serizel
This paper proposes a benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-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…
The Speed Submission to DIHARD II: Contributions & Lessons Learned
Md Sahidullah, Jose Patino, Samuele Cornell +11
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides describing the system, whic…
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…