Sammendrag
Amazonian forests are threatened by numerous anthropogenic pressures not visible by satellite imagery, such as over-hunting or undercover forest degradation. Knowledge of the effects of these degradations is essential for an effective local conservation policy. However, these effects can only be assessed using quantitative methods for monitoring biodiversity in the field. In recent years, ecoacoustics has offered an alternative to traditional techniques with the development of Passive Acoustic Monitoring (PAM) systems allowing, among other things, to automatically monitor species that are difficult to identify by observers, such as crepuscular and nocturnal tropical birds. Although the use of such systems makes it possible to acquire large sets of data collected in the field, it is often difficult to process these data because they generally represent several thousand hours of recordings that need to be annotated and validated manually by an expert with in-depth knowledge of the phenology and behavior of the species studied. The objective of this thesis is to develop a new method to facilitate the work of ecoacousticians in managing large unlabeled acoustic datasets and to improve the identification of potential new taxa. Based on the advancement of Meta-Learning methods and unsupervised learning techniques integrated into the Deep Learning (DL) framework, the Meta Embedded Clustering (MEC) method is proposed to progressively discover and improve the inherent structure of unlabeled data.