Machine learning from training data with a skewed distribution of exampl...
Can we leverage the audiovisual information already present in video to
...
The aim of the Detection and Classification of Acoustic Scenes and Event...
What audio embedding approach generalizes best to a wide range of downst...
Recent studies have put into question the commonly assumed shift invaria...
To reveal the importance of temporal precision in ground truth audio eve...
Real-world sound scenes consist of time-varying collections of sound sou...
Self-supervised representation learning can mitigate the limitations in
...
We introduce the Free Universal Sound Separation (FUSS) dataset, a new c...
We propose a benchmark of state-of-the-art sound event detection systems...
Most existing datasets for sound event recognition (SER) are relatively ...
Performing sound event detection on real-world recordings often implies
...
The study of label noise in sound event recognition has recently gained
...
Label noise is emerging as a pressing issue in sound event classificatio...
This work describes and discusses an algorithm submitted to the Sound Ev...
This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio
t...
As sound event classification moves towards larger datasets, issues of l...
Properly annotated multimedia content is crucial for supporting advances...
This paper describes Task 2 of the DCASE 2018 Challenge, titled
"General...
In the past, Acoustic Scene Classification systems have been based on ha...