01998nam a2200253   4500008004100000020002200041022001400063245006900077210006900146260004400215300001400259490001000273520123900283653001701522653001701539653002301556100002001579700001401599700001701613700001901630700001801649700002201667856005501689       2018                        eng d  a978-3-030-03990-5  a0302-974300aInvestigation of Unsupervised Models for Biodiversity Assessment0 aInvestigation of Unsupervised Models for Biodiversity Assessment  aChambSpringer International Publishing  a160 - 1710 v113203 a<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>
10abioacoustics10abiodiversity10aUnsupervised model1 aMitrovic, Tanja1 aXue, Bing1 aLi, Xiaodong1 aRao, KVSN Rama1 aGarg, Saurabh1 aMontgomery, James  uhttp://link.springer.com/10.1007/978-3-030-03991-202366nas a2200193   4500008004100000245008100041210006900122260001600191300001600207490000600223520178500229653001302014653001702027653001802044100002102062700001802083700002202101856004902123       2018                        eng d00aAutomatic and Efficient Denoising of Bioacoustics Recordings Using MMSE STSA0 aAutomatic and Efficient Denoising of Bioacoustics Recordings Usi  cJan-01-2018  a5010 - 50220 v63 a<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>
10abig data10abioacoustics10aNoise removal1 aBrown, Alexander1 aGarg, Saurabh1 aMontgomery, James  uhttp://ieeexplore.ieee.org/document/8194836/