Zwitscherkasten -- DIY Audiovisual bird monitoring

TL;DR

Zwitscherkasten is a DIY multimodal bird monitoring system that uses deep learning on edge devices for real-time, non-invasive species identification, supporting scalable biodiversity and citizen science efforts.

Contribution

The paper introduces a novel DIY system combining audio and visual data processing on resource-limited hardware for bird monitoring.

Findings

Accurate bird species identification on embedded platforms.

Energy-efficient acoustic activity detection reduces power consumption.

Real-time multimodal classification enables scalable biodiversity monitoring.

Abstract

This paper presents Zwitscherkasten, a DiY, multimodal system for bird species monitoring using audio and visual data on edge devices. Deep learning models for bioacoustic and image-based classification are deployed on resource-constrained hardware, enabling real-time, non-invasive monitoring. An acoustic activity detector reduces energy consumption, while visual recognition is performed using fine-grained detection and classification pipelines. Results show that accurate bird species identification is feasible on embedded platforms, supporting scalable biodiversity monitoring and citizen science applications.