Signal, Statistics and Learning [S2A]

The skills of the Signal, Statistics and Learning (S2A) team are organized in four strategic themes, all relating to data analysis:

1. Machine learning
2. Probability and statistics
3. Signal processing and audio data analysis
4. Automatic analysis of social signals

Four strategic themes:

Machine learning

Machine learning is a field that lies at the interface of mathematics and computer science. It aims to develop concepts and algorithms for the automatic analysis of big data, usually for prediction purposes.
Machine learning group

Probability and statistics

Probability and statistics is a field that brings together all the techniques for the estimation and quantification of uncertainty, based on probabilistic modeling and statistical inference.
Probability and statistics group

Audio Data Analysis and Signal Processing

Audio Data Analysis and Signal Processing is a field that relates to methods for data decomposition, representation learning and parametric modeling for audio signals and data in various applications such as source separation, musical information retrieval (MIR), the analysis of sound scenes, musical acoustics, the analysis of physiological signals, especially electroencephalographic (EEG) signals, and the transformation of audio signals.
Audio Data Analysis and Signal Processing (ADASP) group
Link to the ADASP website

Automatic analysis of social signals

Automatic analysis of social signals is a field that focuses on the computational models of social interaction analysis in web analytics, opinion analytics and social robotics.
Social computing group