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# System prepended metadata

title: AAP-M2_KIM-DLS Internship Proposal

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# AAP-M2_KIM-DLS Internship Proposal

## Title: Deep Learning based methods for audio event detection of Zebras in the Wild.

### Background

The detection of specific behaviour of animals in the wild is useful for many scenarios in the field of ecology and biodiversity sciences. 

@Simon maybe add the project partners here (+Laurent?)

At these institutes at the University of Montpellier, we recently focused on one original way to study animal behaviour, in a non-invasive way by using passive acoustic data recorded in the wild using low power audio logging devices.
The assumption is that this passive acoustic data can be used to understand animal behaviour, as it provides sufficient information for describing spatial‐temporal patterns. 
From this field experiment, we gathered a unique dataset that consists of over XXXXh of continuous audio recordings, accompanied by data from several other modalities such as accelerometer (gyro?) sensors and GPS data.

Scientific research for exploring and exploring large audio datasets have shown that, given enough labeled examples, machine learning could help to detect acoustic events in passive acoustic data. Very recently, deep neural networks have emerged as a promising solution to this problem, yielding in state-of-the-art results. These investigations have yet to be done for zoology (?). 

### The project

In this project we propose to study, in depth, new types of models for their application in computational ecoacoustics.
In particular, we will focus on the learning and interpretability of deep neural networks on acoustic data and their extension to other types of input data (motion sensors, GPS, time stamps). The goal of these models is to automatically segment and label the recordings for events linked to specific animal behaviours such as foraging and snorting. Since only small part of the data is labeled, we plan to also investigate the feasibility of self-supervised learning techniques that do not require extensive labeling.

### Working environment:

The internship will be co-supervised by Simon Chamaillé-Jammes, Ecology Researcher at CNRS Centre d'Ecologie Fonctionnelle et Evolutive) and Fabian-Robert Stöter, Ingenieur de recherche in audio machine learning on big data at Montpellier Computer Science Laboratory (LIRMM).
Simon is leading the ecology related tasks in this project and is providing the existing large scale dataset. Fabian is will supervise the intern with respect to audio processing tasks and will introduce the fundamentals of deep learning and its computational frameworks such as pytorch which will be used to address the work.

The intern will be hosted at the LIRMM in close collaboration with researchers and engineers from the ZENITH team which includes researchers and engineers of the Pl@ntNet platform. The intern will have access to high-performance IT resources (GPU) and unique databases (ZebraDB?). A master thesis continuation may be explored if the candidate wishes to pursue the research path.
