Traditional ecological survey methods are expensive and time-consuming as they typically involve a domain expert in the field. As a result of this, continuous audio recordings are playing an ever more critical role in conservation and biodiversity monitoring. However, listening to these recordings is often infeasible, as they can be thousands of hours long. The knowledge of domain experts can be efficiently leveraged using visualization. Traditionally, spectrograms are used to visualize audio data. However, there is a limit to the duration of audio that can fit on a computer screen as a spectrogram. Several techniques have been adapted to overcome this. Each of these techniques has its own set of advantages and disadvantages that make it ideal for some situations but unsuitable for others. In this paper, we propose a novel visualization based on embeddings produced by Frequency Preserving Autoencoders. We evaluate by visually comparing with traditional spectrogram and spectral index false-color spectrogram, using human-generated annotations as a baseline. We find that calls from some species are more consistently visible in our autoencoder-based visualization, while others are more clearly visible in the alternative visualizations. This novel visualization presents a new opportunity to assist ecologists in identifying bird calls in long-duration audio, if calls are difficult to identify using other methods.

Wildlife calls are the best witnesses to the health of ecosystems, if only we know how to listen to them. Efforts to understand and inform restoration of healthy ecosystems with environmental audio recordings languish from insufficient tools to learn and identify sounds in recordings. To address this problem, we designed and playtested the Bristle Whistle Challenge prototype with ten players. We explored how to design delightful interactions with audio for gaining awareness of nature sounds and supporting wildlife conservation through citizen science. We found that rather than presenting audio alone, it was necessary to connect sounds to other senses and experiences in creative ways to impart meaning and enhance engagement. We offer recommendations to design creative and contextual interactions with media to build awareness of nature's wonders. We call for greater efforts in interaction design to engage people with nature, which is the key to turning around our environmental crisis.

Continuous audio recordings are playing an ever more important role in conservation and biodiversity monitoring, however, listening to these recordings is often infeasible, as they can be thousands of hours long. Automating analysis using machine learning is in high demand. However, these algorithms require a feature representation. Several methods for generating feature representations for these data have been developed, using techniques such as domain-specific features and deep learning. However, domain-specific features are unlikely to be an ideal representation of the data and deep learning methods often require extensively labeled data.In this paper, we propose a method for generating a frequency-preserving autoencoder-based feature representation for unlabeled ecological audio. We evaluate multiple frequency-preserving autoencoder-based feature representations using a hierarchical clustering sample task. We compare this to a basic autoencoder feature representation, MFCC, and spectral acoustic indices. Experimental results show that some of these non-square autoencoder architectures compare well to these existing feature representations.This novel method for generating a feature representation for unlabeled ecological audio will offer a fast, general way for ecologists to generate a feature representation of their audio, which does not require extensively labeled data.

Acoustic sensing has been hailed as a game-changer for detecting furtive wildlife, but uptake has been constrained by the laborious process of reviewing resultant torrents of audio data. To inform the design of interactive interfaces for reviewing audio recordings, we explored how people interact with aural and visual media about birds. We observed how twelve participants with different levels of interest in birds engaged with vocalization recordings, visualizations of bird calls, photographs, and range maps of three species. By conducting thematic analysis, we identified a variety of Challenges of Exploration and Benefits of a Media Assortment. We contribute lessons for designing to Bridge Knowledge & Context and to Facilitate Long-term Engagement with audio in ways that are fun, accessible, and informative. We provide explicit guidance for designers to diversify how citizen scientists interact with nature through audio as they move from engagement to conservation action.

This paper investigates connecting people in remote communities through nature in order to foster stewardship and conservation of endangered species. Global citizen science technologies have found success in urban, developed countries, but they typically rely on large distributed populations to gather or analyze data and do not suit sparsely populated and remote contexts. We undertook a long-term field study to iteratively co-design a tangible and playful nature engagement prototype in a remote World Heritage Area community. The prototype design fosters learning through ambient sounds as well as exploration and discovery of species through nature soundscape recordings. We found that the prototypes amplified locals' interest, became embedded in community relations and gradually led to placemaking of new engagement 'spaces' and of newer forms. We contribute lessons learned on how design can foster nature engagement and stewardship of endangered species by heeding Suchman's call for design to "enter networks of relations that make technology possible". We contribute design implications and new design foci HCI/Citizen science engagement for species conservation.

Ecoacoustics draws together computer scientists and ecologists to achieve an understanding of ecosystems and wildlife using acoustic recordings of the environment. Computer scientists are challenged to manage increasingly large datasets while developing analytic and visualisation tools. Ecologists struggle to find and use tools that answer highly heterogeneous research questions. These two fields are naturally drawn together at the tool interface, however, less attention has been paid to how their practices influence tool design and use. We interviewed and collected email correspondence from four computer scientists and eight ecologists to learn how their practices indicate opportunities for reconciling difference through design. We found that different temporal rhythms, relationships to data, and data-driven questions demand tool configuration, data integration, and standardisation. This research outlines interfacing opportunities for new ecological research utilising large acoustic datasets, and also contributes to evolving HCI approaches in areas making use of big data and human-in-the-loop processes.

An increasing variety of technologies are being developed to support conservation of endangered wildlife; however, comparatively little attention has been devoted to their design. We undertook three years of ethnographic fieldwork and design research with the recovery team of an endangered Australian bird (the Eastern bristlebird) to explore the team's culture and practices, as well as their perspectives on including collection and analysis of environmental acoustic recordings into their conservation praxis. Through thematic analysis, we identified the team's collective goals, culture, conservation activities, and technology use. We found that acoustic technologies have promise for supporting conservation of furtive and vocal Eastern bristlebirds. Trialing acoustic technologies also revealed that the team had strong interest in their use. We identified knowledge gaps, time constraints, and technology aversion as barriers to be overcome with future interaction design research. We offer an initial set of practical guidelines for designing technologies to support conservation.

This paper presents co-design fiction as an approach to engaging users in imagining, envisioning and speculating not just on future technology but future life through co-created fictional works. Design fiction in research is often created or written by researchers. There is relatively little critical discussion of how to co-create design fictions with end-users, with the concomitant opportunities and challenges this poses. To fill this gap in knowledge, we conducted co-design fiction workshops with nine older creative writers, utilising prompts to inspire discussion and engage their imaginative writing about the trend towards tracking and monitoring older people. Their stories revealed futures of neither dystopia nor utopia but of social and moral dilemmas narrating their wish not just to "maintain their independence", but a palpable desire for adventure and very nuanced senses of how they wish to take control. We discuss inherent tensions in the control of the co-design fiction process; balancing the author's need for freedom and creativity with the researcher's desire to guide the process toward the design investigation at hand.

We introduce the IoT Un-Kit Experience, a co-design approach that engages people in exploring, designing and generating personally meaningful IoT applications and that also serves as a means to explore IoT kit design through in-home workshops. Un-Kit represents a seemingly uncompleted set of sensors, actuators and media elements that have a decontextualized appearance - unfinished state, undefined purpose and unboxed form. The approach emphasises users contemplating and experiencing the IoT elements in their familiar space through detailed and layered conversation with researchers; rather than focusing on connecting up the kit itself, thus their ideas are not constrained by the kit or their competence with it. We illustrate the approach through in-home workshops with older adults, envisioned users of IoT who have had limited voice in its conception. The Un-kit approach supported participants to lead the process and to imagine new artfully integrated designs, with personally legible interactions and aesthetic qualities that fit their desire. We offer insights for a more situated and responsive approach to design of the IoT and its constituent kits.

The increasing loss of species globally calls for effective monitoring tools and strategies to inform conservation action. The dominant approach to citizens engagement has been smart phone and platform-centric, tasking crowds to collect and analyze data. However, many critically endangered species inhabit remote areas, characterized by sparsely populated communities with poor internet connectivity. Approaches need to garner high engagement relative to population size, with data collection and knowledge synthesis suited to the local context. We conducted a field study in remote communities to understand how to enhance conservation of Bhutan's critically endangered White-bellied heron by exploring existing monitoring practices and trialing acoustic sensing technologies. We found that knowledge about the species is partial, heterogeneous, situated within and across communities and rooted in cultural beliefs. Sensors, acoustic interfaces, and playful probes provided new ways for the community to 'see' and discuss their local environment fostering them to share and grow their knowledge together. We contribute a synthesis of key considerations for designing effective participatory sensing to conserve species in remote communities.

This research explores the recognition of choruses of two co-existing sibling frog species in long-duration field recordings using false-colour spectrograms and acoustic indices. Acid frogs are a group of endemic frogs that are particularly sensitive to habitat change and competition from other species. The Wallum Sedgefrog (Litoria olongburensis) is the most threatened acid frog species facing habitat loss and degradation across much of their distribution, in addition to further pressures associated with anecdotally-recognised competition from their sibling species, the Eastern Sedgefrogs (Litoria fallax). Monitoring the calling behaviours of these two species is essential for informing L. olongburensis management and protection, and for obtaining ecological information about the process and implications of their competition. Considering the cryptic nature of L. olongburensis and the sensitivity of their habitat to human disturbance, passive acoustic monitoring is a suitable method for monitoring this species. However, manually processing the large quantities of acoustic data collected using these methods is time-consuming and not feasible in the long-term. Therefore, there is a high demand for automated acoustic recognition tools to efficiently search months of recordings and identify target species. Our research provides more insight on how to choose acoustic features that efficiently recognise species from large-scale field-collected recordings at a larger scale. The experimental results show that these techniques are useful in identifying choruses of the two competitive frog species with an accuracy of 76.7% on identifying four acoustic patterns (whether the two species occurred).

Design research is generative, intuitive, experiential, and tactical. Documenting the design research process helps to communicate these decisions, judgements, and values that are embodied in design products. Yet, practices for documenting design research are underreported in the CHI community, particularly for immersive design research field studies. We contribute the "Debrief O'Clock" fieldnote practice for documenting design research field studies, comprising collaborative discussion sessions and the production of written research accounts. We show how the Debrief O'Clock practice emerged in the context of a Digital Community Noticeboard project with a very remote Australian Aboriginal community, and explain three key purposes of Debrief O'Clock as: 1) an early stage data recording and analysis process; 2) a tactical manoeuvre in responsive project planning; and 3) a mechanism for personal debriefing and reflexivity. We conclude with a series of open practical, ethical, and methodological questions to advance the discussion of design research documentation practices.

Passive acoustic recording has great potential for monitoring both endangered and pest species. However, the automatic analysis of natural sound recordings is challenging due to geographic variation in background sounds in habitats and species calls. We have designed and deployed an acoustic sensor network constituting an early warning system for a vocal invasive species, in particular cane toads. The challenging nature of recognising toad calls and the big data arising from sound recording gave rise to a novel edge computing system which permits both effective monitoring and flexible experimentation. This is achieved through a multi-stage analysis system in which calls are detected and progressively filtered, to both reduce data communication needs and to improve detection accuracy. The filtering occurs across different stages of the cloud system. This permits flexible experimentation, for example when a new call or false positive is received. Furthermore, to balance the loss of data from aggressive filtering (call recognition), novel overview techniques are employed to provide data summaries. In this way an end user can receive alerts that a toad call is present, the system can be tuned on the fly, and the user can view summary data to have confidence that the system is functioning correctly. The system has been deployed and is in day-to-day use. The novel approaches taken are applicable to other edge computing systems, which analyse large data streams looking for infrequent events and the system has application for monitoring other vocal species.

Monitoring the environment with acoustic sensors is now practical; sensors are sold as commercial devices, storage is cheap, and the field of ecoacoustics is recognized as an effective way to scale monitoring of the environment. However, a pressing challenge faced in many eScience projects is how to manage, analyze, and visualize very large data so that scientists can benefit, with ecoacoustic data presenting its own particular challenges. This paper presents a new zoomable interactive visualization interface for the exploration of environmental audio data. The interface is a new tool in the Acoustic Workbench, an ecoacoustics software platform built for managing environmental audio data. This Google Maps like interface for audio data, enables zooming in and out of audio data by incorporating specialized, multiresolution, visual representations of audio data into the workbench website. The `zooming' visualization allows scientists to surface the structure, detail, and patterns in content that would otherwise be opaque to them, from scales of seconds through to weeks of data. The Ecosounds instance of the Acoustic Workbench contains 52 years (108 TB) of audio data, from 1016 locations, which results in a 180 million-tile, 8.3 terapixel visualization. The design and implementation of this novel big audio data visualization is presented along with some design considerations for storing visualization tiles.

Free to read on publisher's website Automatically detecting koalas in the real-life environment from audio recordings will immensely help ecologists, conservation groups, and government departments interested in their preservation and the protection of their habitat. Inspired by the success of deep learning approaches in various audio classification tasks, in this paper, the feasibility of recognizing koalas’ calls using a convolutional recurrent neural network architecture (CNN+RNN) is studied. The benefit of this architecture is twofold: firstly, convolutional layers learn local time-frequency patterns from the audio spectrogram and secondly, recurrent layers model longer temporal dependencies of the extracted features. In our datasets, the performance of CNN+RNN is evaluated and compared with standard convolutional neural networks (CNNs). The experimental results show that hybrid CNN+RNN architecture is beneficial for learning long-term patterns in spectrogram exhibited by koalas’ calls in unseen conditions. The proposed method is also applicable for detecting other animal calls such as bird sound where it achieves 87.46% area under curve score on the bird audio detection challenge evaluation data.

This paper presents the concept of technology individuation and explores its role in design. Individuation expresses how, over time, a technology becomes personal and intimate, unique in purpose, orchestrated in place, and how people eventually come to rely on it to sustain connection with others. We articulate this concept as a critical vantage point for designing augmented everyday objects and the Internet of Things. Individuation foregrounds aspects of habituation, routines and arrangements that through everyday practices reveal unique meaning, reflect self-identity and support agency.

We propose the concept of "Situational When", an approach to understanding time in interface design not as a point on a calendar or clock, but as a set of converging circumstances that constitute "the time" for happenings to take place. Time is encoded both explicitly and implicitly in designed products. However, many technologies propagate business-centric, modernist values such as scheduling and efficiency, and marginalize broader socio-cultural aspects on which many activities are nonetheless contingent, e.g. the right people, the right weather conditions, and the right vibe. We derive our reflections from a case study of a cross-cultural digital noticeboard designed with an Australian Aboriginal community. Attention to the situational when opens up new possibilities for design that put greater emphasis on the social and relational aspects of time, the situational insights embodied in local narratives, and the tangible (e.g. people) and intangible (e.g. energy) circumstances that together make up the "right" time.

Passive acoustic recording has great potential for monitoring soniferous endangered and cryptic species. However, this approach requires analysis of long duration environmental acoustic recordings that span months or years. There is a variety of approaches to analysing acoustic data. However, it is unclear which approaches are best suited for monitoring of endangered species in the wild. Specifically, this study is undertaking acoustic monitoring of the critically endangered White-bellied Heron (Ardea insignis) in Bhutan. Four different acoustic analysis methods are investigated in terms of their detection accuracy, involvement of human experts, and overall utility to ecologists for target species monitoring work. Our experimental results show that human pattern detection using a visualization technique has detection performance on par with a cluster-based recogniser, while a machine learning classifier implemented using the same acoustic features suffers from very low precision. Further, specific cases of false positives and false negatives by the different methods are investigated and discussed in terms of their overall utility for ecological monitoring. Based on our experimental results, we demonstrate how an integrated semi-automated approach of human visual pattern analysis with a recogniser is a robust system for acoustic monitoring of target species.

Acoustic recordings have been shown to be an effective way to conduct avian species surveys, whereby a trained expert listens to the audio and records observations, a task that can be very time consuming. In practice, most identification of species are first made by visual inspection of the spectrogram, with listening then performed for verification. This paper presents an approach for a surveyor to rapidly scan long duration recordings of environmental audio by automatically filtering parts with low activity and repetitions of the same call types. Recordings are segmented into fixed-length one-second non-overlapping clips. A classifier filters segments of low activity using features that are robust to different levels of background noise. The non-silent segments are then clustered using a feature representation derived from Time-domain Cepstral Coefficients, calculated from the discrete Fourier transform of downsampled spectrogram rows. This time-invariant feature representation allows for arbitrary segmentation, which is advantageous because segmentation of complex audio soundscapes into individual events is difficult and prone to errors. A visualization tool displays a representative segment from each cluster, grouped hierarchically, allowing an ecological researcher to very rapidly visually scan through the entire variety of audio events that occurred throughout the long recording, without wasting time on silent portions of the recording or repetitions of the same call-type. This tool provides functionality missing from both time-consuming audio players and black-box pattern recognizers, allowing conservation scientists to visually explore the entirety of their recordings.

Acoustic classification of frogs has received increasing attention for its promising application in ecological studies. Various studies have been proposed for classifying frog species, but most recordings are assumed to have only a single species. In this study, a method to classify multiple frog species in an audio clip is presented. To be specific, continuous frog recordings are first cropped into audio clips (10 seconds). Then, various time-frequency representations are generated for each 10-s recording. Next, instead of using traditional hand-crafted features, various features are extracted using pre-trained networks using three time-frequency representations: Fast-Fourier spectrogram, Constant-Q transform spectrogram, and Gammatone-like spectrogram. Finally, a binary relevance based multi-label classification approach is proposed to classify simultaneously vocalizing frog species with our proposed features. Our proposed method is verified using eight frog species widely distributed in Queensland, Australia. The results show that the proposed features extracted via pre-trained networks can achieve better classification performance when compared to hand-crafted features for classifying multiple simultaneously vocalizing species.

Cane-toads (Bufo marinus) are introduced to Australia to negate the insect pests. However, cane-toad population is out of control, and has highly affected native frog species for its great reproductive capacity. Therefore, it is necessary to monitor cane-toads and control their population. Recent advances in acoustic sensors make it possible to monitor cane-toads over long periods. In this paper, we present a novel approach to analyze cane-toad calls for long-term monitoring. Specifically, recorded acoustic data is first transformed into spectrogram using short-time Fourier transform. Then, cane-toad calls are recognized with an oscillation detector. Next, recognized cane-toad calls are annotated for further analysis. Initial analysis of our method demonstrates promising results, with 71.36% true positive rate. With the oscillation detector, cane-toad calling activity is investigated over the whole year.

Smart everyday objects could support the wellbeing, independent living and social connectedness of ageing people, but their successful adoption depends upon them fitting with their skills, values and goals. Many technologies fail in this respect. Our work is aimed at designs that engage older people by building on their individual affective attachment to habituated objects and leveraging, from a participatory design perspective, the creative process through which people continuously adapt their homes and tools to their own lifestyle. We contribute a novel analytic framework based on an analysis of related research on appropriation and habituated objects. It identifies steps in appropriation from inspection to performance and habituation. We test this framework with the preliminary testing of an augmented habituated object, a messaging kettle. While only used in one home so far, its daily use has provoked many thoughts, scenarios and projections about use by friends, both practical, utopian and dystopian.

In this paper we discuss results of a field study focused on understanding the ways money and financial issues are handled within family settings. Families develop 'systems' or methods through which they coordinate and manage their everyday financial activities. Through an analysis of our fieldwork data collected from fifteen families, we provide several examples of such systems, highlighting their qualities and illustrating how such systems come to support the handling of financial activities in the home. Our results show that these systems are developed with a careful consideration of familial values, relationships and routines; and incorporate the use of physical and digital tools. Consequently, we suggest that design should consider the use and non-use of technology when supporting household financial management, taking into account the richness of families' existing organically formed practices surrounding financial systems. Finally, our findings point to the fact that financial management in the domestic setting is socially organized and is closely connected to supporting everyday household activities.

In experimental research using computation, a workflow is a sequence of steps involving some data processing or analysis where the output of one step may be used as the input of another. The processing steps may involve user-supplied parameters, that when modified, result in a new version of input to the downstream steps, in turn generating new versions of their own output. As more experimentation is done, the results of these various steps can become numerous. It is important to keep track of which data output is dependent on which other generated data, and which parameters were used. In many situations, scientific workflow management systems solve this problem, but these systems are best suited to collaborative, distributed experiments using a variety of services, possibly batch processing parameter sweeps. This paper presents an R package for managing and navigating a network of interdependent data. It is intended as a lightweight tool that provides some visual data provenance information to the experimenter to allow them to manage their generated data as they run experiments within their familiar scripting environment, where it may not be desirable to commit to a fully-blown comprehensive workflow manager. The package consists of wrapper functions for writing and reading output data that can be called from within the R analysis scripts, as well as a visualization of the data-output dependency graph rendered within the R-studio console. Thus, it offers benefit to the experimenter while requiring minimal commitment for integration in their existing working environment.

A prototype "messaging kettle" is described. The connected kettle aims to foster communication and engagement with an older friend or relative who lives remotely, during the routine of boiling the kettle. We describe preliminary encounters and findings from demonstrating a working prototype in morning tea gatherings of people in their 50s-late 70s and from introducing it into the homes of two people in their 80s who live on another continent. Key findings are that: The concept of keeping in touch around a "habituated object" such as a kettle was well received; Simple and varied interaction modalities that allow asymmetric forms of communication are needed; Designing for use across different time zones requires attention; And, that even when augmenting a habituated object, the process of introduction, appropriation and habituation still needs significant attention and investigation.

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Bioacoustic monitoring has become a significant research topic for species diversity conservation. Due to the development of sensing techniques, acoustic sensors are widely deployed in the field to record animal sounds over a large spatial and temporal scale. With large volumes of collected audio data, it is essential to develop semi-automatic or automatic techniques to analyse the data. This can help ecologists make decisions on how to protect and promote the species diversity. This paper presents generic features to characterize a range of bird species for bird vocalisation retrieval. In the implementation, audio recordings are first converted to spectrograms using short-time Fourier transform, then a ridge detection method is applied to the spectrogram for detecting points of interest. Based on the detected points, a new region representation are explored for describing various bird vocalisations and a local descriptor including temporal entropy, frequency bin entropy and histogram of counts of four ridge directions is calculated for each sub-region. To speed up the retrieval process, indexing is carried out and the retrieved results are ranked according to similarity scores. The experimental results show that our proposed feature set can achieve 0.71 in term of retrieval success rate which outperforms spectral ridge features alone (0.55) and Mel frequency cepstral coefficients (0.36).

This paper explores an emerging paradigm for HCI design research based primarily upon engagement, reciprocity and doing. Much HCI research begins with an investigatory and analytic ethnographic approach before translating to design. Design may come much later in the process and may never benefit the community that is researched. However in many settings it is difficult for researchers to access the privileged ethnographer position of observer and investigator. Moreover rapid ethnographic research often does not seem the best or most appropriate course of action. We draw upon a project working with a remote Australian Aboriginal community to illustrate an alternative approach in Indigenous research, where the notion of reciprocity is first and foremost. We argue that this can lead to sustainable designs, valid research and profound innovation.

In our large library of annotated environmental recordings of animal vocalizations, searching annotations by label can return thousands of results. We propose a heat map of aggregated annotation time and frequency bounds, maintaining the shape of the annotations as they appear on the spectrogram. This compactly displays the distribution of annotation bounds for the user's query, and allows them to easily identify unusual annotations. Key to this is allowing zero values on the map to be differentiated from areas where there are single annotations.

This paper investigates engaging experienced birders, as volunteer citizen scientists, to analyze large recorded audio datasets gathered through environmental acoustic monitoring. Although audio data is straightforward to gather, automated analysis remains a challenging task; the existing expertise, local knowledge and motivation of the birder community can complement computational approaches and provide distinct benefits. We explored both the culture and practice of birders, and paradigms for interacting with recorded audio data. A variety of candidate design elements were tested with birders.

Acoustic sensing is a promising approach to scaling faunal biodiversity monitoring. Scaling the analysis of audio collected by acoustic sensors is a big data problem. Standard approaches for dealing with big acoustic data include automated recognition and crowd based analysis. Automatic methods are fast at processing but hard to rigorously design, whilst manual methods are accurate but slow at processing. In particular, manual methods of acoustic data analysis are constrained by a 1:1 time relationship between the data and its analysts. This constraint is the inherent need to listen to the audio data. This paper demonstrates how the efficiency of crowd sourced sound analysis can be increased by an order of magnitude through the visual inspection of audio visualized as spectrograms. Experimental data suggests that an analysis speedup of 12× is obtainable for suitable types of acoustic analysis, given that only spectrograms are shown.

Raven and Song Scope are two, state-of-the-art automated sound analysis tools, based on machine learning techniques for detection of species vocalisations. Individually, these systems have been the subject of a number of reviews; however, to date there have been no comparisons made of their relative performance. This paper compares the tools based on six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential applications. Examining these tools, we identified that they fail to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables and clusters them into complex structures.

Traditional sensor networks communicate all data to a central repository for collection and analysis. The data volumes are low so this works: data are moved. Multimedia sensor networks for environmental monitoring present a different scenario. Volumes of data are large and sensors may be located in areas of limited or poor connectivity. Typical cloud solutions do not work. In this paper we present techniques we have used to collect and analyse data from distributed sensors. These include moving some or all data, moving data summaries and moving computation. Where networked bandwidth is low we employ sneaker net to move data from sensors to servers and from servers to volunteers for analysis, in much the same way as advocated by Gray and Szalay.

Acoustic sensors play an important role in augmenting the traditional biodiversity monitoring activities carried out by ecologists and conservation biologists. With this ability however comes the burden of analysing large volumes of complex acoustic data. Given the complexity of acoustic sensor data, fully automated analysis for a wide range of species is still a significant challenge. This research investigates the use of citizen scientists to analyse large volumes of environmental acoustic data in order to identify bird species. Specifically, it investigates ways in which the efficiency of a user can be improved through the use of species identification tools and the use of reputation models to predict the accuracy of users with unidentified skill levels. Initial experimental results are reported.

Abstract Bioinformatics is dominated by online databases and sophisticated web‐accessible tools. As such, it is ideally placed to benefit from the rapid, purpose specific combination of services achievable via web mashups. The recent introduction of a number of sophisticated frameworks has greatly simplified the mashup creation process, making them accessible to scientists with limited programming expertise. In this paper we investigate the feasibility of mashups as a new approach to bioinformatic experimentation, focusing on an exploratory niche between interactive web usage and robust workflows, and attempting to identify the range of computations for which mashups may be employed. While we treat each of the major frameworks, we illustrate the ideas with a series of examples developed under the Popfly framework ‡ . Copyright © 2010 John Wiley & Sons, Ltd.

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data, collaboration, manual, automatic and human-in-the loop analysis.

This work describes recent extensions to the GPFlow scientific workflow system in development at MQUTeR (www.mquter.qut.edu.au), which facilitate interactive experimentation, automatic lifting of computations from single-case to collection-oriented computation and automatic correlation and synthesis of collections. A GPFlow workflow presents as an acyclic data flow graph, yet provides powerful iteration and collection formation capabilities.

The need for large scale environmental monitoring to manage environmental change is well established. Ecologists have long used acoustics as a means of monitoring the environment in their field work, and so the value of an acoustic environmental observatory is evident. However, the volume of data generated by such an observatory would quickly overwhelm even the most fervent scientist using traditional methods. In this paper we present our steps towards realising a complete acoustic environmental observatory - i.e. a cohesive set of hardware sensors, management utilities, and analytical tools required for large scale environmental monitoring. Concrete examples of these elements, which are in active use by ecological scientists, are also presented.

Bioinformatics is dominated by on-line databases and sophisticated web-accessible tools. As such, it is ideally placed to benefit from the rapid, purpose specific combination of services and tools achievable via web mashups. The recent introduction of a number of sophisticated frameworks has greatly simplified the mashup creation process, making them accessible to scientists with limited programming expertise. We investigate the feasibility of mashups as a new approach to bioinformatic experimentation, focusing on an exploratory niche between interactive web usage and robust workflows and attempting to identify the range of computations for which mashups may be employed. While we treat each of the major frameworks, we illustrate the ideas with a series of examples developed under the Popfly framework.

This paper presents the Mobile Collaborative Annotation (MCA) framework, a client-server framework for revision based annotation that enables browser-based offline work and collaborative annotation. The capabilities of MCA are demonstrated by an application case study, Mobile Collaborative Mapping (MCM), an online map mashup that provides scientists, who require geographic information, with a simplified end-to-end mobile system. MCM is a form of mobile Geographic Information System (GIS), but targeted towards end-user customization. MCM extends the online map data model towards the GIS model, and introduces collaborative annotation and tagging to GIS. MCM is location-aware, facilitates offline editing and enables asynchronous synchronization in a collaborative environment.

Abstract Increasingly scientists are using collections of software tools in their research. These tools are typically used in concert, often necessitating laborious and error‐prone manual data reformatting and transfer. We present an intuitive workflow environment to support scientists with their research. The workflow, GPFlow, wraps legacy tools, presenting a high level, interactive web‐based front end to scientists. The workflow backend is realized by a commercial grade workflow engine (Windows Workflow Foundation). The workflow model is inspired by spreadsheets and is novel in its support for an intuitive method of interaction enabling experimentation as required by many scientists, e.g. bioinformaticians. We apply GPFlow to two bioinformatics experiments and demonstrate its flexibility and simplicity. Copyright © 2007 John Wiley & Sons, Ltd.

In this demonstration, we will show how smartphones can be used as a platform for monitoring environmental change, particularly with respect to birdlife. We have researched and implemented a platform using Microsoft smartphones for remotely monitoring birds. The platform comprises smart-phones running a custom application for recording bird song. The application manages sensors in an autonomic fashion to ensure that they operate reliably for long periods of time in a power efficient manner.

Workflow is a key technology for eScience. It enables scientific tools to be composed and the resulting workflows to be managed. Workflow and most other computing tools typically distinguish batch from interactive operation. This distinction is ill-suited to scientific experimentation which typically starts interactively and then may progress to batch operation for larger or repeated runs of experiments. In this paper we present a scientific workflow model which unifies batch and interactive operation. This supports seamless experimentation by scientists. The model is implemented in a web based environment through the Microsoft Windows Workflow system and features a novel model for workflow components.

Workflow is an important enabling technology for eScience. Research into workflow systems for eScience has yielded several specialized workflow engines. In this paper we investigate the nature of scientific workflow and describe how an existing business workflow engine, Microsoft's BizTalk, can be adapted to support scientific workflow, and some advantages of doing this. We demonstrate how BizTalk is able to support scientific workflow through the implementation of two real bioinformatics workflows. These workflows are enacted through a novel Web based interface making them widely accessible.

Computer-based tutoring systems which assist students in solving introductory programming problems have significant potential for improving the quality of programming education and reducing the instructor's work load. The innovative Environment for Learning to Program (ELP) provides an interactive web-based environment for teaching programming to first year Information Technology students at Queensland University of Technology (QUT). ELP allows students to undertake programming exercises by "filling in the gaps" of a partial computer program presented in a web page and to receive guidance in getting their programs to compile and run. Feedback on quality and correctness is provided through a program analysis framework. Students are given the opportunity to produce working programs at the early stages of their course without the need to familiarize themselves with a complex program development environment.