A Hybrid Parametric-Deep Learning Approach for Sound Event Localization and Detection
By Andres Perez-Lopez, Eduardo Fonseca, Xavier Serra. 2019
6 citations recorded by the source corpus.
This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge. The proposed methodology relies on parametric spatial audio analysis for source localization and detection, combined with a deep learning-based monophonic event classifier. The evaluation of the proposed algorithm yields overall results comparable to the baseline system. The main highlight is a reduction of the localization error on the evaluation dataset by a factor of 2.6, compared with the baseline performance.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xavier Serra
- Eduardo Fonseca
- Jordi Pons
- Xavier Favory
- Frederic Font
- Yossi Adi
- Lauri Juvela
- Paavo Alku
In Signal Processing
- Eduardo Fonseca
- Xavier Serra
- Frederic Font
- Xavier Favory
- Jordi Pons
- Andrés Ferraro
- Jakob Abeßer
- D. Ellis
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “2) Network Architectures: Current trends in SER encompass mainly CNNs [87, 104, 112]–[114] and CRNNs [20, 115].”