Evaluating Vector Fusion Techniques for Unsupervised Soundscape Retrieval using Deep Embeddings - Peralta, Andrés D.; Frery, Alejandro C.; Xie, Jie; Colonna, Juan G. (Anais do Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA); 2026: Anais do XVII Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos NaturaisDO - 10.5753/wcama.2026.22589, 2026)
The efficient storage, management, and retrieval of vast volumes of bioacoustic data represent a critical bottleneck for long-term biodiversity monitoring. To address this challenge, this work proposes an unsupervised soundscape retrieval system that integrates high-dimensional embeddings from the pre-trained Perch V2 model with a vector fusion strategy that handles variable-length recordings. We systematically evaluate retrieval efficiency and accuracy by comparing two retrieval algorithms and four vector fusion techniques on a vector database. The methodology was validated using a multitaxonomic dataset comprising birds, mammals, and amphibians. A case study involving eleven species of conservation interest shows that the system significantly outperforms traditional MFCC-based approaches, offering a scalable, robust solution for autonomous biodiversity inventorying.