@conference {58034, title = {Social Network Analysis of an Acoustic Environment: The Use of Visualised Data to Characterise Natural Habitats}, year = {2019}, abstract = {

(1) Background: Ecologists use acoustic recordings for long term environmental monitoring. However, as audio recordings are opaque, obtaining meaningful information from them is a challenging task. Calculating summary indices from recordings is one way to reduce the size of audio data, but the amount of information of summary indices is still too big. (2) Method: In this study we explore the application of social network analysis to visually and quantitatively model acoustic changes. To achieve our aim, we clustered summary indices using two algorithms, and the results were used to generate network maps. (3) Results and Discussion: The network maps allowed us to visually perceive acoustic changes in a day and to visually compare one day to another. To enable quantitative comparison, we also calculated summary values from the social network maps, including Gini coefficient (an economical concept adopted to estimate how unevenly the occurrences are distributed). (4) Conclusion: Social network maps and summary values provide insight into acoustic changes within an environment visually and quantitatively.

}, doi = {10.1109/DICTA47822.201910.1109/DICTA47822.2019.8945963}, url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8943071https://ieeexplore.ieee.org/document/8945963/}, author = {Wang, Junling and Sankupellay, Mangalam and Konovalov, Dmitry and Towsey, Michael and Roe, Paul} } @conference {57915, title = {Field testing and performance evaluation of the Long-term Acoustic Real-Time Sensor for Polar Areas (LARA)}, year = {2019}, abstract = {

In the Arctic, in-situ measurement of the upper water column properties (e.g., salinity and temperature) has been a challenge because of the seasonal sea ice coverage. A Long-term Acoustic Real-Time Sensor for Polar Areas (LARA) was developed, field-tested in the shallow water off Oregon and its performance evaluated. The LARA utilizes a commercially available underwater profiler winch from NiGK, conductivity-temperature-depth (CTD) sensors, and a passive acoustic monitoring (PAM) systems. The sensor section consists of two modules: 1) a satellite antenna with a temperature and depth sensor (TD) and 2) a sensor buoy with a controller, a CTD, and a PAM. Using the two vertically separated temperature and pressure sensors, it is capable of detecting the presence of sea ice by sensing the oceanographic conditions in the upper water column. In addition, using the acoustic sensors, (1) surface wind speed can be estimated from the ambient noise level and (2) enable monitoring of marine mammal calling activity. The winch can be fully controllable from the sensor buoy by acoustic commands. It is capable of aborting an ascent in case sea ice coverage or a high sea state was detected. Although the sea ice detection algorithm could not be tested in Oregon waters, the LARA successfully repeated 94 profiles and transmitted CTD and acoustic detections data via satellite. The wind speed estimates by the PAM were in good agreement with the nearby buoy data. The PAM recorded various biological signals from 10 Hz to 60 kHz including vocalizations of fin, humpback, sperm whales and calls of Pacific white-sided dolphins. The system is capable of repeating 365 profiles or one profile per day for 1 year making it suitable for collecting high-resolution water column data in the extreme weather and water conditions of the Arctic.

}, keywords = {arctic, bio-acoustics, profiling, real-time detections}, author = {H. Matsumoto and J. Haxel and B. Kahn and L. Roche and A. Turpin and Klinck, Holger and R.P. Dziak and J. Childress and K. Sexton and T. Nakamura} } @conference {57652, title = {Automatic Segmentation of Ultrasonic Vocalizations in Rodents}, year = {2019}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Ultrasonic vocalizations studies in rodents have increasingly drawn researchers attention as it have been considered a powerful tool to understand the animals behavior and their interactions in different social and environmental contexts.

This paper presents an entropy-based algorithm for accurate and robust segmentation of mouse ultrasonic calls. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the audio segments accurately. The new approach for mice calls detection has been able to detect up to 97\% of the vocalizations.

}, keywords = {Rodents ultrasonic vocalizations, Signal processing, Signal segmentation, Spectral Entropy}, isbn = {978-3-030-31634-1}, doi = {10.1007/978-3-030-31635-810.1007/978-3-030-31635-8_5}, url = {http://link.springer.com/10.1007/978-3-030-31635-8}, author = {Pessoa, Diogo and Petrella, Lorena and Castelo-Branco, Miguel and Teixeira, C{\'e}sar}, editor = {Henriques, Jorge and Neves, Nuno and de Carvalho, Paulo} } @conference {57584, title = {Statistical parametric synthesis of budgerigar songs}, year = {2019}, abstract = {

In this paper we present the synthesis of budgerigar songs with Hidden Markov Models (HMMs) and the HMM-based Speech Synthesis System (HTS). Budgerigars can produce complex and diverse sounds that are difficult to categorize. We adapted tech- niques that are commonly used in the area of speech synthe- sis so that we can use them for the synthesis of budgerigar songs. To segment the recordings, the songs are broken down into phrases, which are sounds separated by silence. Complex phrases furthermore can be subdivided into smaller units and then be clustered to identify recurring elements. These ele- ment categories along with additional contextual information are used together to enhance the training and synthesis. Over- all, the aim of the process is to offer an interface that gener- ates new sequences and compositions of bird songs based on user input, consisting of the desired song structure and contex- tual information. Finally, an objective evaluation comparing the synthesized output to the natural recording is performed, and a subjective evaluation with human listeners shows that they pre- fer resynthesized over natural recordings and that they perceive no significant differences in terms of naturalness between natu- ral, resynthesized, and synthesized versions.

}, keywords = {bioacoustics, bird song, HMM- based synthesis, speech synthesis}, author = {Gutscher, Lorenz and Pucher, Michael and Lozo, Carina and Hoeschele, Marisa and Mann, Daniel C} } @conference {57418, title = {Why did solid otoliths evolve in the ears of modern bony fishes?}, year = {2019}, abstract = {

Modern bony fishes (Teleostei), which comprise almost one-half of all extant vertebrate species, show a remarkable diversity in ear morphology, including otolith shape. Fish bioacousticians are still puzzling over the question of why solid calcareous otoliths with species-specific shapes evolved, whereas most \“non-teleost\” vertebrates possess numerous tiny otoconia. This question is linked to when and how often a switch from loosely aggregated material towards a solid structure\—and from apatite to calcium carbonate\—occurred during the vertebrate radiation and whether those character shifts are related to altered hearing. In a recent study, we constructed a hypothetical framework for otolith evolution by compiling the available information on the structure of otoliths and otoconia in \> 160 species covering all major vertebrate groups. We concluded that solid teleost otoliths may have initially evolved as a selectively neutral by-product of other key innovations, and that the teleost-specific genome duplication event may have facilitated their subsequent diversification. Differences in otolith mass and shape might have enabled the perception of different ranges of acoustic information and may have evolved during the diversification of auditory abilities. In some teleost groups, otolith morphology may have co-evolved with ancillary hearing structures, especially if ears are closely connected to a gas bladder.

}, keywords = {aragonite, hearing, otoconia, otolith, teleost-specific genome duplication event}, author = {Tanja Schulz-Mirbach and Friedrich Ladich and Martin He{\ss} and Martin Plath} } @conference {57418, title = {Propagation of bird vocalizations in the Alpine environment}, year = {2019}, abstract = {

Rock ptarmigan is a bird living in the mountains tops, whose population tends to decrease in Europe over the last decade, notably due to climate change, habitat loss and human disturbance. Censusing of number of birds in the Alps is realized via point-count monitoring using birds vocalization. However, detection of bird vocalizations by the human hear is somewhat tricky in these non-homogeneous landforms, due to sound propagation effects. In order to improve the counting method, it is necessary to understand how bird vocalizations propagate in the Alpine environment. With this aim, a propagation model based on a ray-tracing approach is developed and includes topography, ground reflection and absorption and atmospheric refraction and absorption. The model is first validated in both frequency and time domains against reference results obtained with a numerical solution of the linearized Euler equations for simple cases. Propagation of the bird vocalization in the Alpine environment is then illustrated. It is shown that the active space of the bird dramatically depends on the environment. The modeling of active and detection space will provide precise information for the birds monitoring, enabling managers to define more precisely their counting plans.

}, keywords = {ethology, Outdoor propagation, Ray tracing}, author = {Lo{\"\i}c Berger and Fr{\'e}d{\'e}ric Sebe} } @conference {57418, title = { The Cormorant Ear {\textendash} Adapted to Underwater Hearing?}, year = {2019}, abstract = {

Diving birds may spend several minutes underwater during foraging dives. However, surprisingly little is known about avian underwater hearing. We do not know their sensitivity or even if they respond to underwater sound. To help filling this gap we measured the audiograms of cormorants (Phalacrocorax carbo sinensis) and studied their ear anatomy. Wild-caught fledglings were anesthetized and their auditory brainstem response (ABR) to clicks and tone bursts was measured, first in an anechoic box in air and then in a large water-filled tank with their head and neck submerged 10 cm below the surface. The overall shape of their air-audiograms was like that reported for birds of the same size in air. The bandwidth and slopes of their audiograms were similar in air and water. However, in air the highest sensitivity was found at 2 kHz, whereas it was displaced towards lower frequencies underwater. These results suggest that cormorants have rather poor in-air hearing compared to similar-sized birds. Their underwater hearing sensitivity, however, is higher than what would have been expected for purely air-adapted ears. A possible reason for the poor in-air sensitivity is the special ear anatomy with the central eardrum shaped as a rigid piston like in turtles.

}, keywords = {Audiogram, Auditory threshold curves, Great cormorant}, author = {Magnus Wahlberg} } @conference {57418, title = {New aspects in birdsong recognition utilizing the gabor transform}, year = {2019}, abstract = {

In this paper we will be concerned with mathematical methods for birdsong recognition and classification. Current approaches compute the spectrogram of an audio recording using the Gabor transform, which is then used as input for a convolutional neural network (CNN) to classify the recording. While recent work is dedicated to finding the best hyperparameters for training the CNN and for data augmentation, the parameters for the Gabor transform and the signal detection methods receive less attention. We aim to close this gap by evaluating the effect of different window lengths on the overall classification accuracy. Additionally we propose a method for denoising signals based on quantiles to account for different signal to noise ratios in the dataset.

}, keywords = {bioacoustics, Deep learning, Gabor transform}, author = {Sven HEUER and Pavel TAFO and Hajo HOLZMANN and Stephan Dahlke} } @conference {57418, title = {Applying convolutional neural networks to the analysis of mouse ultrasonic vocalizations}, year = {2019}, abstract = {

Many species in diverse taxonomic groups, including rodents, bats, and insects, communicate with complex ultrasonic vocalizations (USVs) (\>20 kHz). Two main components of processing and analyzing USV recordings include detection and classification of syllable types. Recently we developed an efficient algorithm for detecting mouse USVs (Automatic Mouse Ultrasound Detector (A-MUD)). The main challenge is detecting USVs under conditions with a low signal-to-noise ratio, while minimizing rates of false positives (FP). Mice produce many short USVs (\< 10 millisecond) that inflate FPs. We aimed to improve the detection of mouse USVs with A-MUD by classifying vocalizations into three discrete syllable types (with 0, 1, or \≥2 frequency-jumps) or FP. Supervised Convolutional Neural Networks (CNNs) were fed by 2D Gammatone Spectrograms (GSs) adapted to the frequency range of mice. Evaluation of performance shows that CNNs yielded an overall accuracy of 95\±1.2\% and macro-F1 score of 90\±2.7\%. In contrast, multilayer feed-forward neural networks fed by vectorized spectrograms provided an overall accuracy of only 85.4\±1.9\% and macro-F1 score of 75.4\±2.9\%, and therefore, the chosen CNNs outperformed this conventional classification method.

}, keywords = {Deep learning, Image-based classification, Ultrasonic vocalizations (USVs)}, author = {Reyhaneh Abbasi and Peter Balazs and Anton NOLL and Doris NICOLAKIS and Maria Adelaide Marconi and Sarah M. Zala and Dustin J. Penn} } @conference {57418, title = {Adaptation of British Standards to Identify Construction Site Activity Noise Sources with the Potential to Cause Stress to Giant Pandas}, year = {2019}, abstract = {

Giant pandas have a broader hearing range than humans and have higher sensitivity in different frequency bands. Giant pandas are known to become stressed when subjected to undesirable noise, therefore noise which humans may deem unobtrusive has the potential to cause stress in giant pandas. As giant panda breeding programmes are taking place in zoos throughout the world it is important to understand the impact that anthropogenic noise has on these mammals and how it affects their wellbeing. This paper discusses the adaptation from international standards on noise to appropriately address the impact from construction site noise on giant pandas.

}, author = {Murray SNAITH and Alessandro RODRIGUEZ and Brett MARMO} }