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Automatic and efficient denoising of bioacoustics recordings using MMSE STSA


Brown, A and Garg, S and Montgomery, J, Automatic and efficient denoising of bioacoustics recordings using MMSE STSA, IEEE Access, 6 pp. 5010-5022. ISSN 2169-3536 (2017) [Refereed Article]


Copyright Statement

Copyright 2017 IEEE.

DOI: doi:10.1109/ACCESS.2017.2782778


Automatic recording and analysis of bird calls is becoming an important way to understand changes in bird populations and assess environmental health. An issue currently proving problematic with the automatic analysis of bird recordings is interference from noise that can mask vocalisations of interest. As such, noise reduction can greatly increase the accuracy of automatic analyses and reduce processing work for subsequent steps in bioacoustics analyses. However, only limited work has been done in the context of bird recordings. Most semiautomatic methods either manually apply sound enhancement methods available in audio processing systems such as SoX and Audacity or apply preliminary filters such as low and highpass filters. These methods are insufficient both in terms of how generically they can be applied and their integration with automatic systems that need to process large amounts of data. Some other work applied more sophisticated denoising methods or combinations of different methods such as Minimum Mean Square Error Short Time Spectral Amplitude estimator (MMSE STSA) and Spectral Subtraction (SS) for other species such as anurans. However their effectiveness is not tested on bird recordings. In this paper, we analyse the applicability of the MMSE STSA algorithm to remove noise from environmental recordings containing bird sounds, particularly focusing on its quality and processing time. The experimental evaluation using real data clearly shows that MMSE STSA can reduce noise with similar effectiveness (using objective metrics such as Predicted Signal Quality (SIG)) to a previously recommended wavelet transform based denoising technique while executing between approximately 5300 times faster depending on the audio files tested.

Item Details

Item Type:Refereed Article
Keywords:birds, bioacoustics, big data, noise reduction, noise measurement, wavelet transforms
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Distributed systems and algorithms
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Terrestrial biodiversity
UTAS Author:Brown, A (Mr Alexander Brown)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:123680
Year Published:2017
Web of Science® Times Cited:11
Deposited By:Information and Communication Technology
Deposited On:2018-01-18
Last Modified:2018-08-06
Downloads:65 View Download Statistics

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