The
ADASP group develops
data analysis methods primarily targeting audio data. These developments rely on signal processing and machine
learning techniques, focusing on:
- data decomposition and representation learning methods, especially sparse representation learning,
- as well as parametric modelling methods.
Such methods are employed essentially to address two types of tasks:
-
source separation,
- human activity-related scene and content analysis, notably using classification methods;
with applications in:
- machine listening,
- music information retrieval,
- audio signal transformation (denoising, enhancement, dereverberation, spatialisation),
- heterogeneous, multiview or multimodal data analysis, especially multimedia content analysis,
- physiological signal analysis, especially M/EEG data,
dereverberation, spatialisation.