02251nas a2200313 4500008004100000245009200041210006900133520124800202653002801450653001701478653003201495653001501527100002701542700002801569700001801597700002301615700003001638700002201668700002101690700002801711700002201739700002101761700001701782700002801799700002601827700002201853700001901875856004301894 2018 eng d00aIt's time to listen: there is much to be learned from the sounds of tropical ecosystems0 aIts time to listen there is much to be learned from the sounds o3 a

Knowledge that can be gained from acoustic data collection in tropical ecosystems is low-hanging fruit. There is every reason to recordand with every day, there are fewer excuses not to do it. In recent years, the cost of acoustic recorders has decreased substantially(some can be purchased for under US$50, e.g., Hillet  al.2018) and the technology needed to store and analyze acoustic data is contin-uously improving (e.g., Corrada Bravoet  al.2017, Xieet  al.2017). Soundscape recordings provide a permanent record of a site at agiven time and contain a wealth of invaluable and irreplaceable information. Although challenges remain, failure to collect acoustic datanow in tropical ecosystems would represent a failure to future generations of tropical researchers and the citizens that benefit fromecological research. In this commentary, we (1) argue for the need to increase acoustic monitoring in tropical systems; (2) describe thetypes of research questions and conservation issues that can be addressed with passive acoustic monitoring (PAM) using both short-and long-term data in terrestrial and freshwater habitats; and (3) present an initial plan for establishing a global repository of tropical recordings.

10aconservation technology10aecoacoustics10apassive acoustic monitoring10aSoundscape1 aDeichmann, Jessica, L.1 aAcevedo-Charry, Orlando1 aBarclay, Leah1 aBurivalova, Zuzana1 aCampos-Cerqueira, Marconi1 ad'Horta, Fernando1 aGame, Edward, T.1 aGottesman, Benjamin, L.1 aHart, Patrick, J.1 aKalan, Ammie, K.1 aLinke, Simon1 aNascimento, Leandro, Do1 aPijanowski, Bryan, C.1 aStaaterman, Erica1 aAide, Mitchell uhttp://doi.wiley.com/10.1111/btp.1259303949nas a2200253 4500008004100000245008100041210006900122260004000191520313300231653002003364653002103384653001703405653002803422653002603450653001503476653001403491653001703505653003103522653001503553653001303568653002403581100002203605856006803627 2018 eng d00aSMART SAMPLING OF ENVIRONMENTAL AUDIO RECORDINGS FOR BIODIVERSITY MONITORING0 aSMART SAMPLING OF ENVIRONMENTAL AUDIO RECORDINGS FOR BIODIVERSIT bQueensland University of Technology3 a

In order to safeguard our environment, we must first have tools that give us information about its health. Biodiversity monitoring is crucial for providing this information. One form of this is bird species richness surveys, which have traditionally been conducted by having an experienced bird-watcher travel to a location to make observations. This is a time-consuming and expensive task.
An emerging alternative approach is to record audio from an ecologically significant location and then listen to it at a later stage for species identification. Unlike on-site surveys, using audio recordings allows the capture of long periods of audio that can then be listened to non-sequentially. Samples can be chosen from throughout the long recording and presented to an ecologist in any order. By selecting this set of samples intelligently, the listener will be able to observe more species in less overall time, increasing the efficiency of their labour.
This thesis investigates methods to make this selection of samples from a long recording in order to maximise the number of bird species contained in a given number of selected samples. The task of species identification remains with the human listener, but the process of selecting the rich subset of the recording is performed by a machine.
We first lay out the framework whereby the problem is decomposed into two parts: how to estimate the internal variety of call types within each candidate sample, and how to estimate the inter-sample dissimilarity. The former allows us to find samples with a high overall number of species and the latter to avoid selecting samples whose species overlap heavily with previously selected samples.
The primary way that these objectives are achieved, and the main contribution of this thesis, is through clustering short segments of audio and using the clusters contained within each sample to estimate the internal variety of call types as well as comparing clusters between samples to estimate their dissimilarity. Two methods of segmentation are used, and, for each one, an appropriate set of preprocessing steps and feature representation was developed. Arising from this pipeline of computational steps is also a novel visualisation tool that can allow a user to rapidly scan long-duration recordings by filtering out parts with low activity and repetition of the same call types.

An algorithm was developed that scores samples based on a combination of their cluster content with other estimates of the internal variety and intra-sample dissimilarity. These other estimates are based on prior knowledge about the daily patterns of vocal activity and durations that species call for at a given time, as well the overall acoustic activity of each sample. The best combination of methods outperforms existing methods, such as randomly sampling from the dawn chorus.
Together, these contributions help to advance the field of ecoacoustics for bird species monitoring and offer a way forward towards developing valuable tools for the exploration of ecological audio recordings.

10aacoustic sensin10aavian monitoring10abioacoustics10aBiodiversity monitoring10abird species richness10aClustering10ae-science10aecoacoustics10ag environmental monitoring10aR Language10asampling10ascientific workflow1 aEichinski, Philip uhttps://eprints.qut.edu.au/123022/1/Philip_Eichinski_Thesis.pdf03307nas a2200313 4500008004100000245010000041210006900141260004000210520226600250653002102516653001802537653001702555653001302572653001202585653001502597653001902612653003302631653001702664653002602681653001302707653004402720653002702764653003402791653002302825653003902848653001802887100002102905856006702926 2018 eng d00aANALYSIS AND VISUALISATION OF VERY-LONG-DURATION ACOUSTIC RECORDINGS OF THE NATURAL ENVIRONMENT0 aANALYSIS AND VISUALISATION OF VERYLONGDURATION ACOUSTIC RECORDIN bQueensland University of Technology3 a

Advances in technology and reduction in data storage costs enable the autonomous collection of large quantities of continuous audio recordings. While the collection of very long environmental recordings has become easier, the analysis of these recordings remains challenging. A very-long-duration audio recording is defined as one with a minimum length of one day, but may have durations of weeks, months, or years. This thesis provides methods for data reduction and visualisation that enable the ecological interpretation and navigation of very-long-duration audio recordings.
The major theme of data reduction commenced after the establishment of protocols and the collection of two thirteen-month continuous audio recordings from two separate Southeast Queensland forest ecosystems. The acoustic indices calculated on one-minute audio segments were used to develop two new techniques to visualise the contents of very- long-duration recordings. An acoustic index is a mathematical expression used to measure a particular aspect of the energy distribution in audio recordings. Microphone failure in one channel was noticed shortly after the recording commenced. A method was established to detect microphone problems in long recordings.
A novel error measure was developed to detect seasonal and site differences and enable optimisation of the clustering based on seasonal and site differences in the data. Cluster interpretation on very-long-duration audio recordings is problematic because listening to large amounts of audio is time-consuming and therefore impractical. To overcome this, a series of five methods were developed to build on the interpretations made through listening. These methods enabled the allocation of an acoustic label to each cluster, resulting in a labelled acoustic sequence. This acoustic sequence was used to develop three additional visualisation techniques.
The culmination of the methods developed in this thesis was the six case studies. These extended the ecological interpretation of the acoustic sequence beyond those that were made through the visualisations. The case studies demonstrated that clustering can facilitate ecological interpretation of very-long-duration audio recordings.

10aacoustic indices10aAnthropophony10abioacoustics10abiophony10aCicadas10aClustering10aData reduction10aDiel plots; Dot-matrix plots10aecoacoustics10aEcological Monitoring10aGeophony10aLong-duration false-colour spectrograms10aMicrophone malfunction10aPrincipal Components Analysis10aSoundscape ecology10aVery-long-duration audio recording10aVisualisation1 aPhillips, Yvonne uhttps://eprints.qut.edu.au/123020/1/Yvonne_Phillips_Thesis.pdf01000nas a2200145 4500008004100000245009800041210006900139520047100208653003000679653004500709653001700754100001900771700002300790856004100813 2018 eng d00aTo bee or not to bee: Investigating machine learning approaches for beehive sound recognition0 aTo bee or not to bee Investigating machine learning approaches f3 a

In this work, we aim to explore the potential of machine learning methods to the problem of beehive sound recognition. A major contribution of this work is the creation and release of annotations for a selection of beehive recordings. By experimenting with both support vector machines and convolutional neural networks, we explore important aspects to be considered in the development of beehive sound recognition systems using machine learning approaches.

10abeehive sound recognition10aComputational bioacoustic scene analysis10aecoacoustics1 aNolasco, Inês1 aBenetos, Emmanouil uhttps://arxiv.org/pdf/1811.06016.pdf01310nas a2200205 4500008004100000245005800041210005700099520071700156653001800873653002500891653001700916653001500933653002100948100002000969700001400989700001901003700001901022700001701041856004601058 2018 eng d00aNIPS4Bplus: a richly annotated birdsong audio dataset0 aNIPS4Bplus a richly annotated birdsong audio dataset3 a

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated record- ings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of record- ings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species spe- cific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird popula- tion monitoring, species classification, birdsong vocalisation detection and classification.

10aaudio dataset10abird vocalisa- tions10aecoacoustics10aecosystems10arich annotations1 aMorfi, Veronica1 aBas, Yves1 aPamuła, Hanna1 aGlotin, Hervé1 aStowell, Dan uhttps://bio.acousti.ca/zh-hans/node/53274