@book {53385,
	title = {Investigation of Unsupervised Models for Biodiversity Assessment},
	volume = {11320},
	year = {2018},
	pages = {160 - 171},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {<p>Significant animal species loss has been observed in recent decades due to habitat destruction, which puts at risk environmental integrity and biodiversity. Traditional ways of assessing biodiversity are limited in terms of both time and space, and have high cost. Since the presence of animals can be indicated by sound, recently acoustic recordings have been used to estimate species richness. Bioacoustic sounds are typically recorded in habitats for several weeks, so contain a large collection of different sounds. Birds are of particular interest due to their distinctive calls and because they are useful ecological indicators. To assess biodiversity, the task of manually determining how many different types of birds are present in such a lengthy audio is really cumbersome. Towards providing an automated support to this issue, in this paper we investigate and propose a clustering based approach to assist in automated assessment of biodiversity. Our approach first estimates the number of different species and their volumes which are used for deriving a biodiversity index. Experimental results with real data indicates that our proposed approach estimates the biodiversity index value close to the ground truth.</p>
},
	keywords = {bioacoustics, biodiversity, Unsupervised model},
	isbn = {978-3-030-03990-5},
	issn = {0302-9743},
	doi = {10.1007/978-3-030-03991-210.1007/978-3-030-03991-2_17},
	url = {http://link.springer.com/10.1007/978-3-030-03991-2},
	author = {KVSN Rama Rao and Garg, Saurabh and Montgomery, James},
	editor = {Mitrovic, Tanja and Xue, Bing and Li, Xiaodong}
}
@article {52675,
	title = {Automatic and Efficient Denoising of Bioacoustics Recordings Using MMSE STSA},
	journal = {IEEE Access},
	volume = {6},
	year = {2018},
	month = {Jan-01-2018},
	pages = {5010 - 5022},
	abstract = {<p>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 vocalizations 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 high-pass 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 for other species such as anurans. However, their effectiveness is not tested on bird recordings. In this paper, we analyze 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 5\&ndash;300 times faster depending on the audio files tested.</p>
},
	keywords = {big data, bioacoustics, Noise removal},
	doi = {10.1109/ACCESS.2017.2782778},
	url = {http://ieeexplore.ieee.org/document/8194836/},
	author = {Brown, Alexander and Garg, Saurabh and Montgomery, James}
}
