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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach.
Forrest Briggs1, Balaji Lakshminarayanan, Lawrence Neal
1Department of Electrical Engineering & Computer Science, Oregon State University, Corvallis, Oregon 97331, USA. briggsf@eecs.oregonstate.edu
The Journal of the Acoustical Society of America
|June 21, 2012
Summary
This study introduces a new machine learning method for identifying multiple bird species in audio recordings. The approach accurately detects species occurrence, aiding ecological monitoring and conservation efforts.
Area of Science:
- Bioacoustics
- Machine Learning
- Computational Ecology
Background:
- Field recordings often contain overlapping vocalizations from multiple bird species.
- Acoustic species classification in complex field settings is underexplored.
Purpose of the Study:
- To develop a machine learning framework for classifying multiple species in audio recordings.
- To propose a novel algorithm for transforming audio signals into a representation suitable for multi-instance multi-label (MIML) classification.
Main Methods:
- Formulated acoustic species classification as a multi-instance multi-label (MIML) problem.
- Developed a MIML bag generator for audio using 2D time-frequency segmentation.
- Utilized unattended omnidirectional microphones for data collection in the H. J. Andrews Experimental Forest.
Main Results:
- Achieved high accuracy (96.1% true positives/negatives) in classifying species presence.
- Demonstrated the effectiveness of the proposed MIML framework and audio representation.
- Successfully separated overlapping bird sounds using time-frequency segmentation.
Conclusions:
- Automated bird species detection using MIML is highly accurate.
- This method has significant potential for long-term ecological monitoring.
- Applications include species distribution modeling and conservation planning.
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