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Bioacoustic signal denoising: a review

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Abstract

Animal biodiversity has been experiencing rapid decline due to various reasons such as habitat loss and degradation, invasive species, and environment pollution. Recent advances in acoustic sensors provide a novel way to monitor animals through investigating collected bioacoustic recordings. To accurately monitor animals, the precondition is the high performance of developed bioacoustic signal recognition model. However, since bioacoustic recordings are often obtained in an open environment, various sources of noise will affect the audio quality, which causes problems for automated analysis of animal sound recordings. Although various methods have been developed for addressing the noise in different bioacoustic recordings, to the best of our knowledge, there is still no paper that reviews and summarizes those methods. The main aim of this paper is to provide a systematic survey of the existing literature related to bioacoustic signal denoising. By investigating the existing denoising methods for bioacoustic recordings, current challenges, possible opportunities, and future research directions are discussed and concluded.

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Notes

  1. https://github.com/Jerry-Jie-Xie/BioacousticSignalDenoising.

  2. https://cis.whoi.edu/science/B/whalesounds/index.cfm.

  3. https://www.ecosounds.org/.

  4. https://archive.ics.uci.edu/ml/datasets/Anuran+Calls+%28MFCCs%29.

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

  6. http://www.avianz.net/.

  7. https://en.wikipedia.org/wiki/Xeno-canto.

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Acknowledgements

This work is supported by the 111 Project. This work is also supported by Fundamental Research Funds for the Central Universities (Grant No: JUSRP11924) and Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology (Grant No: FM-2019-06). This work is partially supported by National Natural Science Foundation of China (Grant No: 61902154). This work is also partially supported by Natural Science Foundation of Jiangsu Province (Grant No: BK2019043526) and Jiangsu Province Post Doctoral Fund (Grant No: 2020Z430). We also want to thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for the institutional support.

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  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, People’s Republic of China

    Jie Xie

  2. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Jie Xie

  3. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Jie Xie

  4. Instituto de Computação (IComp), Universidade Federal do Amazonas (UFAM), Av. General Rodrigo Octávio, 6200, CEP: 69080-900, Manaus, Amazonas, Brasil

    Juan G. Colonna

  5. School of Computer Science, Queensland University of Technology, Brisbane, Australia

    Jinglan Zhang

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Xie, J., Colonna, J.G. & Zhang, J. Bioacoustic signal denoising: a review. Artif Intell Rev 54, 3575–3597 (2021). https://doi.org/10.1007/s10462-020-09932-4

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