Automated bird acoustic event detection and robust species classification

Journal Article

Title: Automated bird acoustic event detection and robust species classification
Publication Date:
May 01, 2017
Journal: Ecological Informatics
Volume: 39
Pages: 99-108
Publisher: Elsevier
Receptor:

Document Access

Website: External Link

Citation

Zhao, Z.; Zhang, S.; Xu, Z.; Bellisario, K.; Dai, N.; Omrani, H.; Pijanowski, B. (2017). Automated bird acoustic event detection and robust species classification. Ecological Informatics, 39, 99-108.
Abstract: 

Non-invasive bioacoustic monitoring is becoming increasingly popular for biodiversity conservation. Two automated methods for acoustic classification of bird species currently used are frame-based methods, a model that uses Hidden Markov Models (HMMs), and event-based methods, a model consisting of descriptive measurements or restricted to tonal or harmonic vocalizations. In this work, we propose a new method for automated field recording analysis with improved automated segmentation and robust bird species classification. We used a Gaussian Mixture Model (GMM)-based frame selection with an event-energy-based sifting procedure that selected representative acoustic events. We employed a Mel, band-pass filter bank on each event's spectrogram. The output in each subband was parameterized by an autoregressive (AR) model, which resulted in a feature consisting of all model coefficients. Finally, a support vector machine (SVM) algorithm was used for classification. The significance of the proposed method lies in the parameterized features depicting the species-specific spectral pattern. This experiment used a control audio dataset and real-world audio dataset comprised of field recordings of eleven bird species from the Xeno-canto Archive, consisting of 2762 bird acoustic events with 339 detected “unknown” events (corresponding to noise or unknown species vocalizations). Compared with other recent approaches, our proposed method provides comparable identification performance with respect to the eleven species of interest. Meanwhile, superior robustness in real-world scenarios is achieved, which is expressed as the considerable improvement from 0.632 to 0.928 for the F-score metric regarding the “unknown” events. The advantage makes the proposed method more suitable for automated field recording analysis.

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