PACT 2018-2019 - Module AdaBoost
Expert
Objectives of this module
- Understand:
- the supervised training paradigm, the boosting paradigm, the decision tree algorithm, the AdaBoost agorithm
- Design an algorithm that
- perform the training of a model given a training dataset
- perform the automatic classification of a given audio track given its audio descriptors and the trained model
Outcomes
- Report explaining
- the supervised training paradigm, the boosting paradigm, the decision tree algorithm, the AdaBoost agorithm
- the design decisions of the final script
- Training and test database for validating the algorithm
- Algorithms in Matlab/Python/Java that
- takes as input the audio descriptors corresponding to a given audio track
- output the recognized class (valence, arousal classes)
Methodology
- Small bibliography review with resources provided by the expert
- Shared repository (brand 'algorithm')
- Test units
- Weekly meeting
Milestones
- 26/11: Soutenances PAN1 et avancement du projet
- 04/02: PAN2: Soutenances de modules, y compris GL, debrief tuteur
- Each module should be developed in a prototype language (Matlab/Python/Java)
- 18/03: Soutenances PAN3
- Each module should be developed in the targeted language (possibly in real-time)
- Report should be provided
- 13/05: Bilan PACT: séance tutorée d’évaluation de collaboration, apprentissages, confrontation au concret. Présence obligatoire.
- Each module should be integrated in the final application
- Update of the report should be provided
- Individual oral examination of each student
Evaluation
- Report quality
- Continuity of work
- Algorithm quality
- Brief oral presentation and questions (each student)
Resources
