30 citations · 47 across the 3 of their papers we have counts for
7 papers
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…