ODAS: Spatial Acoustic Localization for Macroscope Bioacoustic Monitoring

Document ID: CNL-FN-2026-XXX Version: 0.1 Date: February 6, 2026 Author: Michael P. Hamilton, Ph.D.

AI Assistance Disclosure: This field note was developed through morning dialogue with Claude (Anthropic, Claude Opus 4.6). The AI contributed to technical evaluation of the ODAS system, integration concept development, and manuscript drafting. The author takes full responsibility for the content, accuracy, and conclusions.

Abstract

This note documents the discovery and preliminary evaluation of ODAS (Open embeddeD Audition System), an MIT-licensed C library for real-time sound source localization, tracking, and separation using microphone arrays. ODAS offers a potential capability leap for the Macroscope LIFE domain by extending existing BirdWeather species-identification infrastructure with spatial bearing and elevation data, enabling territorial mapping, movement tracking, and dawn chorus spatial structure analysis from passive acoustic monitoring.

1. Background

Current Macroscope bioacoustic monitoring relies on BirdWeather stations equipped with single microphones feeding audio to BirdNET for species identification. This pipeline produces temporally indexed species detections — “Pipilo maculatus detected at 06:47” — but no spatial information. The acoustic landscape is flattened to a point source at the microphone location.

2. ODAS System Description

ODAS (Open embeddeD Audition System) is developed by IntroLab at the Université de Sherbrooke, Québec. The system performs four operations in a real-time pipeline:

1. Sound source localization — azimuth and elevation of detected sound sources via time-delay-of-arrival across a microphone array
2. Tracking — following localized sources across time as they move or persist
3. Separation — isolating individual source signals from the mixed acoustic field
4. Post-filtering — cleaning separated signals for downstream analysis

The library is written entirely in C, optimized for low-cost embedded hardware, and released under the MIT license [1]. A web-based GUI (odas_web) provides real-time visualization of localized sources on a unit sphere.

3. Hardware Requirements

ODAS requires a multi-microphone array rather than a single microphone. IntroLab provides two open-source array designs:

• 8SoundsUSB — eight-input, USB-powered, configurable microphone array [3]
• 16SoundsUSB — sixteen-input, USB-powered, configurable microphone array [4]

Spatial separation between microphones enables time-delay-of-arrival calculations. More microphones yield better angular resolution. Array geometry is configurable via JSON configuration files.

4. Proposed Integration Concept

The integration pipeline extends the existing BirdWeather architecture:

ODAS (localize and separate) → BirdNET (identify species) → Macroscope (map spatially and temporally)

The result transforms species detections from temporal events to spatiotemporal observations: not “P. maculatus at 06:47” but “P. maculatus singing from bearing 215°, elevation 12° at 06:47” — with continuous tracking as the bird moves.

This is a fundamentally different data product. Potential outputs include:

• Territorial boundary mapping from persistent singing locations
• Perch-site fidelity across days and seasons
• Dawn chorus spatial structure — which species occupy which acoustic niches in physical space
• Movement patterns during foraging and territorial defense
• Spatial gradients in acoustic biodiversity across the monitoring area

5. Key Unknowns

Several questions require investigation before deployment:

• Angular resolution — What spatial precision is achievable at typical bird-call frequencies (1–8 kHz) with a reasonably sized outdoor array?
• Outdoor performance — ODAS was developed for robotics applications. Wind, rain, ambient noise, and temperature variation present different challenges than indoor environments.
• Compute requirements — Can a Raspberry Pi run ODAS and BirdNET simultaneously, or does the pipeline need to be split across two boards?
• Effective range — At what distance does localization accuracy degrade to the point of being ecologically uninformative?
• Array weatherproofing — Long-term outdoor deployment requires environmental protection without compromising acoustic performance.

6. Macroscope Context

This capability would operate within the LIFE domain but has cross-domain implications. Spatial acoustic data could correlate with EARTH domain variables (temperature gradients, wind patterns, vegetation structure) to reveal how physical landscape features shape acoustic ecology. The sensor network philosophy — extending human observational capacity through instrumentation — applies directly: no human observer can simultaneously track the spatial positions of multiple singing birds across a dawn chorus.

References

[1] Grondin, F., Létourneau, D., Godin, C., Lauzon, J.-S., Vincent, J., Michaud, S., Faucher, S., & Michaud, F. (2022). “ODAS: Open embeddeD Audition System.” Frontiers in Robotics and AI, Volume 9. https://www.frontiersin.org/article/10.3389/frobt.2022.854444

[2] Grondin, F. & Michaud, F. (2019). “Lightweight and Optimized Sound Source Localization and Tracking Methods for Opened and Closed Microphone Array Configurations.” Robotics and Autonomous Systems. https://arxiv.org/pdf/1812.00115

[3] IntroLab (2024). “8SoundsUSB.” https://sourceforge.net/projects/eightsoundsusb/

[4] IntroLab (2024). “16SoundsUSB.” https://github.com/introlab/16SoundsUSB

[5] IntroLab (2024). “ODAS: Open embeddeD Audition System.” https://github.com/introlab/odas

Document History

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