02163nas a2200217 4500008004100000245011800041210006900159520138300228653001701611653001901628653001701647653002101664653002201685100003201707700002801739700002801767700003101795700002401826700002801850856006701878 2019 eng d00aDataset on audio records of animals from the northeast Andes of Colombia I: The bird sounds of Boyacá Department0 aDataset on audio records of animals from the northeast Andes of 3 a

This dataset is the first effort to combine the audio biodiversity of a taxonomic group in a selected location, the Boyacá department in Colombia. We conducted a detailed review of the sound recordings for birds from the Boyacá department within three repositories, the environmental sound collection of the Humboldt Institute, the Macaulay Library of the Cornell Lab of Ornithology, and the xeno-canto platform of the Naturalis Biodiversity Center. We selected recordings that were identified up to species and have the complete metadata information. Using latitude and longitude information, we assigned each recording to one of the three regions and one of the 12 biotic units reported for Boyacá. We reported a total of 2321 recordings belonging to the Andean region (1892), Orinoquian region (425), and Carare-Lebrija-Nechi-Sinu (4). The sounds of Boyacá birds have been sampled for approximately three decades, with two peaks of activity in early 2000’s and 2018. We also included a map with the distribution of biotic units and sound recordings of our dataset. This dataset can be used to extract acoustic traits to test hypotheses of turnover in the acoustic space or traits by species, or to compare acoustic traits between species. It can also allow decision-makers to support biodiversity-based economies such as avitourism.

10abioacoustics10abird acoustics10aecoacoustics10asound collection10asounds recordings1 aBuitrago-Cardona, Alexandra1 aColón-Piñeiro, Zuania1 aBorja-Acosta, Kevin, G.1 aOspina-Larrea, Ana, María1 aGaleano, Sandra, P.1 aAcevedo-Charry, Orlando uhttps://linkinghub.elsevier.com/retrieve/pii/S235234091931296X01392nas a2200241 4500008004100000245005800041210005700099520070900156653001800865653002800883653001700911653001900928653002300947653001700970653001500987653002101002100002001023700001401043700001901057700001901076700001701095856003801112 2019 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 recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific 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 population monitoring, species classification, birdsong vocalisation detection and classification.

10aaudio dataset10aAudio signal processing10abioacoustics10aBioinformatics10aBird vocalisations10aecoacoustics10aecosystems10arich annotations1 aMorfi, Veronica1 aBas, Yves1 aPamuła, Hanna1 aGlotin, Hervé1 aStowell, Dan uhttps://peerj.com/articles/cs-22302082nas a2200193 4500008004100000245008100041210006900122520148200191653002101673653002201694653001701716653001701733653001701750100001901767700002401786700001801810700001901828856004101847 2019 eng d00aSounding Soil: An Acoustic, Ecological & Artistic Investigation of Soil Life0 aSounding Soil An Acoustic Ecological Artistic Investigation of S3 a

The interdisciplinary Sounding Soil project explores the soil’s soundscape and renders experienceable and investigable the activity and diversity of soil life in an artistic-acoustic observatory. The main aim is to increase soil awareness in the general population as well as among decision-makers involved in soil policies and food producers. While moving through or digging the soil matrix, the soil fauna produces noises. Moreover, some animals seem to use the soil as a communication medium, forming a complex soundscape. Land use and agricultural management may have marked effects on the soil soundscape. Thus, the (acoustic) richness of a local soil animal community may serve as an indicator of the functioning of a soil ecosystem. In the scientific module of the Sounding Soil project, we implement and test acoustic indices to assess soil biodiversity and community composition. In the art and citizen science module, several Swiss farmers and subsequently the broader public record the soundscapes of their soils with a low-cost recording device. The result is a sound art installation as a publicly accessible observatory, integrating our scientific findings with recordings by participants in the citizen science module. This article describes the character of soil soundscapes in agricultural land and forests and reports on our research design and first insights into the relations between land use, soil types, biodiversity, and soil soundscapes.

10aacoustic ecology10aArtistic Research10abioacoustics10aecoacoustics10aSoil Ecology1 aMaeder, Marcus1 aGossner, Martin, M.1 aKeller, Armin1 aNeukom, Martin uhttps://bio.acousti.ca/sv/node/5760202790nas a2200217 4500008004100000245012400041210006900165520202600234653002802260653001702288653001702305653002902322653001902351653002302370100002402393700002402417700001602441700002402457700002402481856006702505 2020 eng d00ahardRain: An R package for quick, automated rainfall detection in ecoacoustic datasets using a threshold-based approach0 ahardRain An R package for quick automated rainfall detection in 3 a

The increasing demand for cost-efficient biodiversity data at large spatiotemporal scales has led to an increase in the collection of large ecoacoustic datasets. Whilst the ease of collection and storage of audio data has rapidly increased and costs fallen, methods for robust analysis of the data have not developed so quickly. Identification and classification of audio signals to species level is extremely desirable, but reliability can be highly affected by non-target noise, especially rainfall. Despite this demand, there are few easily applicable pre-processing methods available for rainfall detection for conservation practitioners and ecologists. Here, we use threshold values of two simple measures, Power Spectrum Density (amplitude) and Signal-to-Noise Ratio at two frequency bands, to differentiate between the presence and absence of heavy rainfall. We assess the effect of using different threshold values on Accuracy and Specificity. We apply the method to four datasets from both tropical and temperate regions, and find that it has up to 99% accuracy on tropical datasets (e.g. from the Brazilian Amazon), but performs less well in temperate environments. This is likely due to the intensity of rainfall in tropical forests and its falling on dense, broadleaf vegetation amplifying the sound. We show that by choosing between different threshold values, informed trade-offs can be made between Accuracy and Specificity, thus allowing the exclusion of large amounts of audio data containing rainfall in all locations without the loss of data not containing rain. We assess the impact of using different sample sizes of audio data to set threshold values, and find that 200 15 s audio files represents an optimal trade-off between effort, accuracy and specificity in most scenarios. This methodology and accompanying R package ‘hardRain’ is the first automated rainfall detection tool for pre-processing large acoustic datasets without the need for any additional rain gauge data.

10aAcoustic pre-processing10abioacoustics10aecoacoustics10aEnvironmental monitoring10aRain detection10aSoundscape ecology1 aMetcalf, Oliver, C.1 aLees, Alexander, C.1 aBarlow, Jos1 aMarsden, Stuart, J.1 aDevenish, Christian uhttps://linkinghub.elsevier.com/retrieve/pii/S1470160X1930787303949nas 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.pdf