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Dictionary Learning for Bioacoustics Monitoring with Applications to Species Classification

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Abstract

This paper deals with the application of the convolutive version of dictionary learning to analyze in-situ audio recordings for bio-acoustics monitoring. We propose an efficient approach for learning and using a sparse convolutive model to represent a collection of spectrograms. In this approach, we identify repeated bioacoustics patterns, e.g., bird syllables, as words and represent new spectrograms using these words. Moreover, we propose a supervised dictionary learning approach in the multiple-label setting to support multi-label classification of unlabeled spectrograms. Our approach relies on a random projection for reduced computational complexity. As a consequence, the non-negativity requirement on the dictionary words is relaxed. Furthermore, the proposed approach is well-suited for a collection of discontinuous spectrograms. We evaluate our approach on synthetic examples and on two real datasets consisting of multiple birds audio recordings. Bird syllable dictionary learning from a real-world dataset is demonstrated. Additionally, we successfully apply the approach to spectrogram denoising and species classification.

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  1. https://www.kaggle.com/c/mlsp-2013-birds

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Acknowledgments

This work is partially supported by the National Science Foundation grants CCF-1254218, DBI-1356792, IIS-1055113, and the Colciencias’ Doctoral Training Support Programme. The authors thank the anonymous reviewers for the valuable comments and suggestions.

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Authors and Affiliations

  1. Universidad Nacional de Colombia, Manizales, 170004, Colombia

    J. F. Ruiz-Muñoz

  2. School of EECS, Oregon State University, Corvallis, OR, 97331-5501, USA

    Zeyu You, Raviv Raich & Xiaoli Z. Fern

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  1. J. F. Ruiz-Muñoz
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  2. Zeyu You
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  4. Xiaoli Z. Fern
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Correspondence to J. F. Ruiz-Muñoz.

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Ruiz-Muñoz, J.F., You, Z., Raich, R. et al. Dictionary Learning for Bioacoustics Monitoring with Applications to Species Classification. J Sign Process Syst 90, 233–247 (2018). https://doi.org/10.1007/s11265-016-1155-0

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