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Technologies 2018, 6(1), 19; doi:10.3390/technologies6010019

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

School of Computing, Science & Engineering, University of Salford, Manchester M5 4WT, UK
This paper is an extended version of our paper published in Proceedings of the Seventh International Conference on Innovative Computing Technology (INTECH 2017), Luton, UK, 16–18 August 2017.
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Received: 15 December 2017 / Revised: 21 January 2018 / Accepted: 25 January 2018 / Published: 28 January 2018
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Abstract

Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications. View Full-Text
Keywords: microphone; wind noise; matrix decomposition and reconstruction; separability; weighted correlation; acoustic sensing; acoustic signals; environmental noise; monitoring microphone; wind noise; matrix decomposition and reconstruction; separability; weighted correlation; acoustic sensing; acoustic signals; environmental noise; monitoring
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Eldwaik, O.; F. Li, F. Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method. Technologies 2018, 6, 19.

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