"Pipistrellus pipistrellus" and "Pipistrellus pygmaeus" in the Iberian Peninsula: an annotated segmented dataset and a proof of concept of a classifier in a real environment

Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound signals can be simultaneously recorded, and this makes species location and identification a long and difficult task. This field of research could be greatly improved through the use of bioacoustics which provide a more accurate automated detection, identification and count of the wildlife of a particular area. We have analyzed the calls of two bat species—Pipistrellus pipistrellus and Pipistrellus pygmaeus—both of which are common types of bats frequently found in the Iberian Peninsula. These two cryptic species are difficult to identify by their morphological features, but are more easily identified by their echolocation calls. The real-life audio files have been obtained by an Echo Meter Touch Pro 1 bat detector. Time-expanded recordings of calls were first classified manually by means of their frequency, duration and interpulse interval. In this paper, we first detail the creation of a dataset with three classes, which are the two bat species but also the silent intervals. This dataset can be useful to work in mixed species environment. Afterwards, two automatic bat detection and identification machine learning approaches are described, in a laboratory environment, which represent the previous step to real-life in an urban scenario. The priority in that approaches design is the identification using short window analysis in order to detect each bat pulse. However, given that we are concerned with the risks of automatic identification, the main aim of the project is to accelerate the manual ID process for the specialists in the field. The dataset provided will help researchers develop automatic recognition systems for a more accurate identification of the bat species in a laboratory environment, and in a near future, in an urban environment, where those two bat species are common

Detecting Bat Calls from Audio Recordings

Bat monitoring is commonly based on audio analysis. By collecting audio recordings from large areas and analysing their content, it is possible estimate distributions of bat species and changes in them. It is easy to collect a large amount of audio recordings by leaving automatic recording units in nature and collecting them later. However, it takes a lot of time and effort to analyse these recordings. Because of that, there is a great need for automatic tools. We developed a program for detecting bat calls automatically from audio recordings. The program is designed for recordings that are collected from Finland with the AudioMoth recording device. Our method is based on a median clipping method that has previously shown promising results in the field of bird song detection. We add several modifications to the basic method in order to make it work well for our purpose. We use real-world field recordings that we have annotated to evaluate the performance of the detector and compare it to two other freely available programs (Kaleidoscope and Bat Detective). Our method showed good results and got the best F2-score in the comparison

Assessing and Analyzing Bat Activity with Acoustic Monitoring: Challenges and Interpretations

Acoustic monitoring is a powerful technique for learning about the ecology of bats, but understanding sources of variation in the data collected is important for unbiased interpretation. The objectives of this dissertation were to investigate sources of variation in acoustic monitoring and make recommendations for acoustic survey design and analysis. I addressed this goal in three ways: i) variation resulting from differences in bat detectors, ii) methods for objective identification of peak activity, and iii) the use of stationary transects to address within-site spatial variation. First, I compared variation of detection of echolocation calls among commonly available bat detectors and found significant differences in distance and angle of detection. Consequently, this source of variation should be taken into account when comparing datasets obtained with different systems. Furthermore, choice of detector should be taken into account when designing new studies. Second, I investigated two statistical methods for identifying peaks in activity, percentile thresholds and space-time scan statistic (SaTScan). Acoustic monitoring provides a relative measure of activity levels and is rarely evaluated based on objective criteria, so describing bat activity as “high” or “low” is useful only in context of the studies in question. Percentile thresholds allow for peaks to be identified relative to a larger distribution of activity levels. SaTScan identifies peaks in space and time that are significantly higher than the background expectation of the dataset. Both methods are valuable tools for replicable and objective identification of peak activity that can be applied at various temporal and spatial scales. Third, I examine how within-site spatial variation can impact estimates of bat activity. I used a stationary transect of bat detectors to i) assess variation in patterns of activity at each detector, ii) test whether spatial or temporal factors were more important for explaining variation in activity, iii) explore what sampling effort in space and time is required for species-specific activity levels. The picture of activity differs significantly within a site depending on detector placement so it is important to use multiple detectors simultaneously to collect accurate estimates of activity

The acoustic ecology of the Ghost Bat (Macroderma gigas) : form, function and applied uses of vocalisations

The ghost bat (Macroderma gigas) is a large, carnivorous echolocating bat endemic to Northern Australia. The species resembles no other bat in the world, weighing 150 g with distinctive pale fur, large ears and noseleaf, and translucent wings, measuring 60 cm across, that inspired its common name. The ghost bat’s population is in decline, likely as a result of roost disturbance by human visitors and destruction of roost habitats by mining. Little is known about ghost bat ecology and behaviour, and this lack of information hinders the application of effective conservation management actions. Studies of the ghost bat are complicated by the species’ extreme sensitivity to roost visitation, which can readily cause roost abandonment and mortality. The ghost bat is a highly vocal species with a number of documented social vocalisations of unknown function. I hypothesised that the ecology and behaviour of the ghost bat could be studied through the medium of their social vocalisations. For my thesis, I collected acoustic and behavioural data from across the ghost bat’s range in the Northern Territory, Australia, over a period of three years. I conducted playback experiments, phylogenetic analysis, and captive species observations to investigate the acoustic ecology of the ghost bat and examine how the species’ acoustic behaviour could be used to help conserve this threatened bat. My thesis provides the most in-depth insight into the acoustic ecology of the ghost bat to date, with the creation of a permanent database of the ghost bat’s complex vocal repertoire and an ethogram of solitary and social behaviours with associated vocalisations, demonstrating the functional complexity of the ghost bat’s communication system. In particular, I provide the first evidence of song in the repertoire of the ghost bat and the first record of mobbing behaviour in this species. My study adds to the depauperate knowledge on dialects in bats, demonstrating how geographic isolation can result in structural changes of social vocalisations due to drift. Importantly, I trialled and demonstrated the applicability of social vocalisations as proxies for non- vocal behaviour and the use of acoustic playback to study and capture ghost bats away from the roost. I hope that the insights in this thesis positively affect the conservation outcome for the ghost bat and enable the collection of the necessary information to design effective conservation plans without causing further detrimental impacts to at-risk colonies

Algorithmic Analysis of Complex Audio Scenes

In this thesis, we examine the problem of algorithmic analysis of complex audio scenes with a special emphasis on natural audio scenes. One of the driving goals behind this work is to develop tools for monitoring the presence of animals in areas of interest based on their vocalisations. This task, which often occurs in the evaluation of nature conservation measures, leads to a number of subproblems in audio scene analysis. In order to develop and evaluate pattern recognition algorithms for animal sounds, a representative collection of such sounds is necessary. Building such a collection is beyond the scope of a single researcher and we therefore use data from the Animal Sound Archive of the Humboldt University of Berlin. Although a large portion of well annotated recordings from this archive has been available in digital form, little infrastructure for searching and sharing this data has been available. We describe a distributed infrastructure for searching, sharing and annotating animal sound collections collaboratively, which we have developed in this context. Although searching animal sound databases by metadata gives good results for many applications, annotating all occurences of a specific sound is beyond the scope of human annotators. Moreover, finding similar vocalisations to that of an example is not feasible by using only metadata. We therefore propose an algorithm for content-based similarity search in animal sound databases. Based on principles of image processing, we develop suitable features for the description of animal sounds. We enhance a concept for content-based multimedia retrieval by a ranking scheme which makes it an efficient tool for similarity search. One of the main sources of complexity in natural audio scenes, and the most difficult problem for pattern recognition, is the large number of sound sources which are active at the same time. We therefore examine methods for source separation based on microphone arrays. In particular, we propose an algorithm for the extraction of simpler components from complex audio scenes based on a sound complexity measure. Finally, we introduce pattern recognition algorithms for the vocalisations of a number of bird species. Some of these species are interesting for reasons of nature conservation, while one of the species serves as a prototype for song birds with strongly structured songs.Algorithmische Analyse Komplexer Audioszenen In dieser Arbeit untersuchen wir das Problem der Analyse komplexer Audioszenen mit besonderem Augenmerk auf natürliche Audioszenen. Eine der treibenden Zielsetzungen hinter dieser Arbeit ist es Werkzeuge zu entwickeln, die es erlauben ein auf Lautäußerungen basierendes Monitoring von Tierarten in Zielregionen durchzuführen. Diese Aufgabenstellung, die häufig in der Evaluation von Naturschutzmaßnahmen auftritt, führt zu einer Anzahl von Unterproblemen innerhalb der Audioszenen-Analyse. Eine wichtige Voraussetzung um Mustererkennungs-Algorithmen für Tierstimmen entwickeln zu können, ist die Verfügbarkeit großer Sammlungen von Aufnahmen von Tierstimmen. Eine solche Sammlung aufzubauen liegt jenseits der Möglichkeiten eines einzelnen Forschers und wir verwenden daher Daten des Tierstimmenarchivs der Humboldt Universität Berlin. Obwohl eine große Anzahl gut annotierter Aufnahmen in diesem Archiv in digitaler Form vorlagen, gab es nur wenig unterstützende Infrastruktur um diese Daten durchsuchen und verteilen zu können. Wir beschreiben eine verteilte Infrastruktur, mit deren Hilfe es möglich ist Tierstimmen-Sammlungen zu durchsuchen, sowie gemeinsam zu verwenden und zu annotieren, die wir in diesem Kontext entwickelt haben. Obwohl das Durchsuchen von Tierstimmen-Datenbank anhand von Metadaten für viele Anwendungen gute Ergebnisse liefert, liegt es jenseits der Möglichkeiten menschlicher Annotatoren alle Vorkommen eines bestimmten Geräuschs zu annotieren. Darüber hinaus ist es nicht möglich einem Beispiel ähnlich klingende Geräusche nur anhand von Metadaten zu finden. Deshalb schlagen wir einen Algorithmus zur inhaltsbasierten Ähnlichkeitssuche in Tierstimmen-Datenbanken vor. Ausgehend von Methoden der Bildverarbeitung entwickeln wir geeignete Merkmale für die Beschreibung von Tierstimmen. Wir erweitern ein Konzept zur inhaltsbasierten Multimedia-Suche um ein Ranking-Schema, dass dieses zu einem effizienten Werkzeug für die Ähnlichkeitssuche macht. Eine der grundlegenden Quellen von Komplexität in natürlichen Audioszenen, und das schwierigste Problem für die Mustererkennung, stellt die hohe Anzahl gleichzeitig aktiver Geräuschquellen dar. Deshalb untersuchen wir Methoden zur Quellentrennung, die auf Mikrofon-Arrays basieren. Insbesondere schlagen wir einen Algorithmus zur Extraktion einfacherer Komponenten aus komplexen Audioszenen vor, der auf einem Maß für die Komplexität von Audioaufnahmen beruht. Schließlich führen wir Mustererkennungs-Algorithmen für die Lautäußerungen einer Reihe von Vogelarten ein. Einige dieser Arten sind aus Gründen des Naturschutzes interessant, während eine Art als Prototyp für Singvögel mit stark strukturierten Gesängen dient

Exploring Animal Behavior Through Sound: Volume 1

This open-access book empowers its readers to explore the acoustic world of animals. By listening to the sounds of nature, we can study animal behavior, distribution, and demographics; their habitat characteristics and needs; and the effects of noise. Sound recording is an efficient and affordable tool, independent of daylight and weather; and recorders may be left in place for many months at a time, continuously collecting data on animals and their environment. This book builds the skills and knowledge necessary to collect and interpret acoustic data from terrestrial and marine environments. Beginning with a history of sound recording, the chapters provide an overview of off-the-shelf recording equipment and analysis tools (including automated signal detectors and statistical methods); audiometric methods; acoustic terminology, quantities, and units; sound propagation in air and under water; soundscapes of terrestrial and marine habitats; animal acoustic and vibrational communication; echolocation; and the effects of noise. This book will be useful to students and researchers of animal ecology who wish to add acoustics to their toolbox, as well as to environmental managers in industry and government