France Job Openings
CEA
STAGE: Self-explainable model for audio identification of bird species H/F
Grenoble
September 13, 2024
Category
Contract
Job title
Subject
Contract duration (months)
Job description
trained (post-hoc explanations), some XAI methods propose to enforce explainability
constraints directly during the design phase of the machine learning process, resulting
in so-called self-explainable models. In this regard, case-based reasoning is currently
considered a viable alternative to more opaque “black box” convolutional neural networks (CNN), with works such as (Chen et al. 2019) or (Nauta, Bree, and Seifert 2021). In case-based reasoning, new instances of a problem are solved by drawing comparisons with examples encountered before, such that the decision is taken and motivated by the fact that the new instance resembles some known cases (also called prototypes).
CEA-LIST has already developed an open-source library for case-based reasoning net-
works, called Ca BRNet(Xu-Darme et al. 2024), and wish to extend its application to new domains and modalities.
classification of bird songs. Audio clips are often encoded in the form of spectrograms, i.e. 2D representations of the intensity of the signal at various frequencies, across a given period of time. Since spectograms can be interpreted as images, a common practice consists in processing them using deep CNNs originally designed for computer vision (Kahl et al. 2021). Hence, the goal of the internship is to extend the case-based reasoning approach to this new task, by adapting existing computer vision methods to learn audio prototypes. In particular, the new approach will take into account the temporal specificities of audio samples. Indeed, contrary to computer vision models which are spacially invariant (the nature of an object remains identical regardless of its position inside the image), spatial location is crucial in spectograms as it corresponds to different frequency ranges and different periods of times.
In practice, the internship will be split in several subtasks as follows:
- Establish a baseline using the reference Bird NET model.
- Identify a body of existing works on self-explainable models for audio classification
- Design and train a case-based reasoning model for audio classifiation, using the Ca BRNet framework.
Methods / Means
Applicant Profile
- Master student or equivalent (2nd/3rd engineering school year) in computer science
- knowledge of Python and the Pytorch framework
- ability to work in a team, some knowledge of version control
- notions of AI and neural networks
- notions of Computer Vision
- notions of explainable AI
Site
Job location
Location
Languages
- English (Fluent)
- French (Fluent)
Prepared diploma
Recommended training
Ph D opportunity
Position start date
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
- defence and security,
- nuclear energy (fission and fusion),
- technological research for industry,
- fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
2024-32911Description de l'unité
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