Thiago S. Gouvêa — accepted AI-conference papers

2026

Making Weak Supervision Interactive: Exploring Transfer from Sound Libraries to Passive Acoustic Monitoring Data

Novruz Mammadli, Rida Saghir, Kanwar Ammar Ali, Prathmesh Doddanawar, Thiago S. Gouvêa, Daniel Sonntag

IJCAI 2026

Passive Acoustic Monitoring (PAM), an increasingly popular method for wildlife monitoring, generates large volumes of data whose analysis depends on instance-level annotations that are costly to obtain. Archival sound collections provide weak labels that lack temporal localisation. In prior work, we

Cited by 0 Scholar ↗

2026

Visualizing and Interacting with Model Representation Space for Human-Centric Active Learning

Rida Saghir, Thiago S. Gouvêa, Daniel Sonntag

IJCAI 2026

Active learning reduces annotation effort by selecting informative samples, yet most approaches remain model-driven, offering users little control over training or support for understanding model behaviour. Human-centric active learning brings users further into the loop by introducing additional po

Cited by 0 Scholar ↗

2024

Demo: Enhancing Wildlife Acoustic Data Annotation Efficiency through Transfer and Active Learning

Hannes Kath, Patricia P. Serafini, Ivan B. Campos, Thiago S. Gouvêa, Daniel Sonntag

IJCAI 2024poster

Passive Acoustic Monitoring (PAM) has become a key technology in wildlife monitoring, generating large amounts of acoustic data. However, the effective application of machine learning methods for sound event detection in PAM datasets is highly dependent on the accessibility of annotated data, a proc…

Cited by 3 Source ↗PDF ↗Scholar ↗

2023

A Human-in-the-Loop Tool for Annotating Passive Acoustic Monitoring Datasets

Hannes Kath, Thiago S. Gouvêa, Daniel Sonntag

IJCAI 2023poster

Deep learning methods are well suited for data analysis in several domains, but application is often limited by technical entry barriers and the availability of large annotated datasets. We present an interactive machine learning tool for annotating passive acoustic monitoring datasets created for w…

Cited by 10 Source ↗PDF ↗Scholar ↗

2023

Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves

Thiago S. Gouvêa, Hannes Kath, Ilira Troshani, Bengt Lüers, Patricia P. Serafini, Ivan B. Campos +6

IJCAI 2023poster

Biodiversity loss is taking place at accelerated rates globally, and a business-as-usual trajectory will lead to missing internationally established conservation goals. Biosphere reserves are sites designed to be of global significance in terms of both the biodiversity within them and their potentia…

Cited by 9 Source ↗PDF ↗Scholar ↗