Meta-Embedded Clustering (MEC): A new method for improving clustering quality in unlabeled bird sound datasets
Résumé
In recent years, ecoacoustics has offered an alternative to traditional biodiversity monitoring techniques with the development of passive acoustic monitoring (PAM) systems allowing, among others, to detect and identify species that are difficult to detect by human observers, automatically. PAM systems typically generate large audio datasets, but using these monitoring techniques to infer ecologically meaningful information remains challenging. In most cases, several thousand hours of recordings need to be manually labeled by experts limiting the operability of the systems. Based on recent developments of meta-learning algorithms and unsupervised learning techniques, we propose here Meta-Embedded Clustering (MEC), a new method with high potential for improving clustering quality in unlabeled bird sound datasets. MEC method is organized in two main steps, with: (a) fine-tuning of a pretrained convolutional neural network (CNN) backbone with different meta-learning al gorithms using pseudo-labeled data, and (b) clustering of manually-labeled bird sounds in the latent space based on vector embeddings extracted from the fine-tuned CNN. The MEC method significantly enhanced average clustering performance from less than 1% to more than 80%, greatly outperforming the traditional approach of relying solely on CNN features extracted from a general neotropical audio database. However, this enhanced performance came with the cost of excluding a portion of the data categorized as noise. By improving the quality of clustering in unlabeled bird sound datasets, the MEC method should facilitate the work of ecoacousticians in managing acoustic units of bird song/call clustered according to their similarities, and in identifying potential clusters of species undetected using traditional approaches.
Auteurs, date et publication :
Auteurs Joachim Poutaraud , Jérôme Sueur , Christophe Thébaud , Sylvain Haupert
Publication : Ecological Informatics
Date : 2025
Volume : 82
Pages : 102687