01854nas a2200169 4500008004100000245007700041210006900118520126800187653001701455653002501472653002101497653002301518100001501541700001801556700002101574856008901595 2019 eng d00aIdentifying Patterns of Human and Bird Activities Using Bioacoustic Data0 aIdentifying Patterns of Human and Bird Activities Using Bioacous3 a

In general, humans and animals often interact within the same environment at the same time. Human activities may disturb or affect some bird activities. Therefore, it is important to monitor and study the relationships between human and animal activities. This paper proposed a system able not only to automatically classify human and bird activities using bioacoustic data, but also to automatically summarize patterns of events over time. To perform automatic summarization of acoustic events, a frequency–duration graph (FDG) framework was proposed to summarize the patterns of human and bird activities. This system first performs data pre-processing work on raw bioacoustic data and then applies a support vector machine (SVM) model and a multi-layer perceptron (MLP) model to classify human and bird chirping activities before using the FDG framework to summarize results. The SVM model achieved 98% accuracy on average and the MLP model achieved 98% accuracy on average across several day-long recordings. Three case studies with real data show that the FDG framework correctly determined the patterns of human and bird activities over time and provided both statistical and graphical insight into the relationships between these two events.

10abioacoustics10aevent classification10ahuman activities10apatterns of events1 aLi, Renjie1 aGarg, Saurabh1 aBrown, Alexander uhttps://www.mdpi.com/1999-4907/10/10/917https://www.mdpi.com/1999-4907/10/10/917/pdf01998nam 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

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.

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

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 5–300 times faster depending on the audio files tested.

10abig data10abioacoustics10aNoise removal1 aBrown, Alexander1 aGarg, Saurabh1 aMontgomery, James uhttp://ieeexplore.ieee.org/document/8194836/