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Source separation in underwater acoustic problems

Source separation in underwater acoustic problems
Source separation in underwater acoustic problems
When conducting passive acoustic monitoring of humpback whale songs in St Marie channel, Madagascar, sometimes recordings containing multiple singers were obtained. In this case, separating the mixtures and obtaining a recording of an individual singer is of interest. The specific method that we utilized for source separation is adapted from the proposal by Sawada et al. This algorithm can effectively operate in conditions with strong reverberation. It can also potentially cope with underdetermined mixtures where source number exceeds hydrophone number.

The effectiveness of the Sawada method is verified through separation of artificial humpback song mixtures generated by the impulse responses of underwater channel model. However, this method is unreliable for the separation of real humpback whale songs as a consequence of severe background noise. We propose a noise reduction method based on weighted median threshold scheme, which significantly improves source separation performance of real recording in severe noise environments.

As to the Sawada algorithm, the number of sources need to be known in order to conduct source separation. However, in reality, the number of source is unknown, Hence, we need to estimate it before performing sources separation. Various methods for automatically estimating the number of sources are investigated in this thesis, and the units counting method turns out to be the most promising one.
University of Southampton
Zhang, Zhenbin
71a39f34-25e9-427e-81eb-766efa6a7e84
Zhang, Zhenbin
71a39f34-25e9-427e-81eb-766efa6a7e84
White, Paul
2dd2477b-5aa9-42e2-9d19-0806d994eaba

Zhang, Zhenbin (2016) Source separation in underwater acoustic problems. University of Southampton, Faculty of Engineering and the Environment, Doctoral Thesis, 173pp.

Record type: Thesis (Doctoral)

Abstract

When conducting passive acoustic monitoring of humpback whale songs in St Marie channel, Madagascar, sometimes recordings containing multiple singers were obtained. In this case, separating the mixtures and obtaining a recording of an individual singer is of interest. The specific method that we utilized for source separation is adapted from the proposal by Sawada et al. This algorithm can effectively operate in conditions with strong reverberation. It can also potentially cope with underdetermined mixtures where source number exceeds hydrophone number.

The effectiveness of the Sawada method is verified through separation of artificial humpback song mixtures generated by the impulse responses of underwater channel model. However, this method is unreliable for the separation of real humpback whale songs as a consequence of severe background noise. We propose a noise reduction method based on weighted median threshold scheme, which significantly improves source separation performance of real recording in severe noise environments.

As to the Sawada algorithm, the number of sources need to be known in order to conduct source separation. However, in reality, the number of source is unknown, Hence, we need to estimate it before performing sources separation. Various methods for automatically estimating the number of sources are investigated in this thesis, and the units counting method turns out to be the most promising one.

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More information

Published date: August 2016
Organisations: University of Southampton, Inst. Sound & Vibration Research

Identifiers

Local EPrints ID: 400600
URI: http://eprints.soton.ac.uk/id/eprint/400600
PURE UUID: a25d87ec-551f-4190-b3cf-3f4ee2bce0cf
ORCID for Paul White: ORCID iD orcid.org/0000-0002-4787-8713

Catalogue record

Date deposited: 29 Sep 2016 13:59
Last modified: 06 Jun 2018 13:12

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