S. S. Sethi

The paper introduces MAARU, a low-cost, open-source multichannel acoustic recorder for spatial ecosystem monitoring, demonstrating its accuracy in localizing sounds and improving species identification.

The paper applies a pre-trained bird vocalisation detection model to global audio datasets, achieving high precision for many species and enabling biodiversity monitoring across multiple spatial and temporal scales.

This study uses deep learning to analyze acoustic data and shows snowmobile noise reduces bird vocalizations in winter and pre-breeding seasons, with some recovery after noise events.

This study uses random forests to predict bird migration patterns based on climate data and eBird surveys, finding that lagged climate features improve predictions in October.

This study investigates the generalisability of soundscape analysis for biodiversity monitoring and finds that acoustic features and machine learning models are inconsistent across datasets.

This paper shows that soundscapes can predict species occurrence in tropical forests without needing extensive labeled data, achieving high accuracy for many species.

The paper presents an open-source, real-time eco-acoustic monitoring network in Borneo's tropical rainforests, providing code, design, and infrastructure for data collection and analysis.

This paper develops a machine learning method using eco-acoustic data to monitor ecosystem changes and detect anomalies efficiently.

The paper introduces an open-source, low-cost ecosystem monitoring system that autonomously captures and transmits continuous data, outperforming existing technologies at a fraction of the cost.

The paper shows CCL3 promotes B cell interactions with follicular regulatory T cells in germinal centers, enhancing their negative regulation.