@article {58170, title = {A two-stage approach to automatically detect and classify woodpecker (Fam. Picidae) sounds}, journal = {Applied Acoustics}, volume = {166}, year = {2020}, month = {Jan-09-2020}, pages = {107312}, abstract = {

Inventorying and monitoring which bird species inhabit a specific area give rich and reliable information regarding its conservation status and other meaningful biological parameters. Typically, this surveying process is carried out manually by ornithologists and birdwatchers who spend long periods of time in the areas of interest trying to identify which species occur. Such methodology is based on the experts\’ own knowledge, experience, visualization and hearing skills, which results in an expensive, subjective and error prone process. The purpose of this paper is to present a computing friendly system able to automatically detect and classify woodpecker acoustic signals from a real-world environment. More specifically, the proposed architecture features a two-stage Learning Classifier System that uses (1) Mel Frequency Cepstral Coefficients and Zero Crossing Rate to detect bird sounds over environmental noise, and (2) Linear Predictive Cepstral Coefficients, Perceptual Linear Predictive Coefficients and Mel Frequency Cepstral Coefficients to identify the bird species and sound type (i.e., vocal sounds such as advertising calls, excitement calls, call notes and drumming events) associated to that bird sound. Conducted experiments over a data set of the known woodpeckers species belonging to the Picidae family that live in the Iberian peninsula have resulted in an overall accuracy of 94,02\%, which endorses the feasibility of this proposal and encourage practitioners to work toward this direction.

}, keywords = {Audio classification, birdsong, Birdsound classification, event detection, Woodpeckers}, issn = {0003682X}, doi = {10.1016/j.apacoust.2020.107312}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0003682X19305080}, author = {Vida{\~n}a-Vila, Ester and Navarro, Joan and Alsina-Pag{\`e}s, Rosa Ma and Ram{\'\i}rez, {\'A}lvaro} } @article {53442, title = {Improved visualization of large temporal series for the evaluation of good environmental status}, journal = {Applied Acoustics}, volume = {148}, year = {2019}, month = {Jan-05-2019}, pages = {55 - 61}, abstract = {

Passive acoustic monitoring of underwater sounds is an emerging discipline that can be used to guarantee that anthropogenic noise meets acceptable limits, to detect the presence of cetacean species, and to ensure sustainable exploitation of our oceans and seas. In this scenario, graphical representation techniques play a key role in helping to reveal seasonal structures of human made noises. Nevertheless, for very long temporal series, it might be challenging to find a graphic visualization technique that allows representing a time range that is long enough to capture these seasonal events, while at the same time preserving short isolated events. We propose a framework for the creation of such visualization techniques and analyze the different stages involved: data reduction, color encoding, and signal processing on graphs. All of this is applied to data from deployments in two marine protected areas in order to provide an acoustic panorama and identify seasonal events.

}, issn = {0003682X}, doi = {10.1016/j.apacoust.2018.12.009}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0003682X18306261https://api.elsevier.com/content/article/PII:S0003682X18306261?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0003682X18306261?httpAccept=text/plain}, author = {Miralles, Ram{\'o}n and Lara, Guillermo and Gosalbez, Jorge and Bosch, Ignacio and Le{\'o}n, Antonio} } @article {53431, title = {A real-time bird sound recognition system using a low-cost microcontroller}, journal = {Applied Acoustics}, volume = {148}, year = {2019}, month = {Jan-05-2019}, pages = {194 - 201}, abstract = {

Monitoring the environment using stand-alone sensor nodes provides valuable information to researchers, practitioners and policy makers. One such problem in this area is bird activity monitoring using its sound patterns. There are several existing devices that can record bird sounds in a region. Then, the stored data is processed off-line. However, the time between the acquisition and processing stages while using such devices is generally of the order of weeks. This approach is not amenable to real-time monitoring. Therefore, there is a need for a system which is able to both monitor the environment and process data on the system itself. The most important attribute of such a system is its power consumption since it operates in the environment only by its battery. In this study, we propose such a stand-alone, energy efficient, low-level, custom-made, real-time bird call processing system concentrated on single-labeled bird calls. The system is composed of a microphone, Texas Instruments Tiva C microcontroller, and a storage unit. The proposed system enables data recording, preliminary on-board signal processing, feature extraction, classification, and data storage. In the proposed system, we simultaneously record and process data. Hence, the system not only stores the environmental sounds, it also classifies the detected birds on-site. The proposed system offers flexibility (both in hardware and software) for expansion.

}, keywords = {Bird call processing, Feature extraction, Mel frequency cepstrum coefficients, Real-time processing, Spectral noise gating}, issn = {0003682X}, doi = {10.1016/j.apacoust.2018.12.028}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0003682X18304213https://api.elsevier.com/content/article/PII:S0003682X18304213?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0003682X18304213?httpAccept=text/plain}, author = {K{\"u}c̣{\"u}ktopcu, Okan and Masazade, Engin and {\"U}nsalan, Cem and Varshney, Pramod K.} } @article {53232, title = {Practical implementation of auditory time and frequency weighting in marine bioacoustics}, journal = {Applied Acoustics}, volume = {145}, year = {2019}, month = {Jan-02-2019}, pages = {137 - 143}, abstract = {

Much effort is currently directed at describing the behavioral reactions of marine mammals following exposure to sound with the aim of deriving generalized thresholds and dose-response functions. The perceived loudness of a given sound is a candidate for a common metric for sound exposure. The loudness of a signal relates to various factors, including the stimulus duration and frequency content, and it can be approximated by an appropriate time and frequency weighting of the signal. Auditory frequency weighting is achieved by applying a frequency weighting function (band-pass filter), with a frequency response resembling the shape of an inverted audiogram. Temporal weighting may be achieved by computing the running rms-average (Leq) with a time constant that is comparable to that of the mammalian auditory system. The practical implementation of such weighting functions are presented in the form of Matlab functions. These functions generate output signals that are weighted according to current recommendations for different groups of marine mammals. With these functions, it is possible to derive the weighted peak Leq of a signal, which is likely to be a good proxy for the loudness of the signal. Ultimately, this weighted level is conjectured to be a predictor of behavioral response of marine mammals to the sound.

}, issn = {0003682X}, doi = {10.1016/j.apacoust.2018.09.022}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0003682X18303967https://api.elsevier.com/content/article/PII:S0003682X18303967?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0003682X18303967?httpAccept=text/plain}, author = {Tougaard, Jakob and Beedholm, Kristian} } @article {47314, title = {Automatic bird sound detection in long real-field recordings: Applications and tools}, journal = {Applied Acoustics}, volume = {80}, year = {2014}, month = {Jan-06-2014}, pages = {1 - 9}, abstract = {

The primary purpose for pursuing this research is to present a modular approach that enables reliable automatic bird species identification on the basis of their sound emissions in the field. A practical and complete computer-based framework is proposed to detect and time-stamp particular bird species in continuous real field recordings. Acoustic detection of avian sounds can be used for the automatized monitoring of multiple bird taxa and querying in long-term recordings for species of interest for research- ers, conservation practitioners, and decision makers, such as environmental indicator taxa and threa- tened species. This work describes two novel procedures and offers an open modular framework that detects and time-stamps online calls and songs of target bird species and is fast enough to report results in reasonable time for non-processed field recordings of many thousands files and is generic enough to accommodate any species. The framework is evaluated on two large corpora of real field data, targeting the calls and songs of American Robin Turdus migratorius , a Northamerican oscine passerine (true song- bird) and the Common Kingfisher Alcedo atthis , a non-passerine species with a wide distribution through- out Eurasia and North Africa. With the aim of promoting the widespread use of digital autonomous recording units (ARUs) and species recognition technologies the processing code and a large corpus of audio recordings is provided in order to enable other researchers to perform and assess comparative experiments.

}, keywords = {bird recognition, birdsong detection, computational ecology}, issn = {0003682X}, doi = {10.1016/j.apacoust.2014.01.001}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0003682X14000024}, author = {Potamitis, Ilyas and Ntalampiras, Stavros and Olaf Jahn and Riede, Klaus} } @article {16462, title = {Two-stage detection of north Atlantic right whale upcalls using local binary patterns and machine learning algorithms}, journal = {Applied Acoustics}, volume = {120}, year = {2017}, month = {Jan-05-2017}, pages = {158 - 166}, abstract = {

In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using contour-based and texture based methods. The former is based on extraction of time\–frequency features from upcall contours, and the latter employs a Local Binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Comparing ROC curves of machine learning algorithms obtained from Cornell University\’s dataset reveals that LBP features improved performance accuracy up to 43\% over time\–frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve highest upcall detection rates with LBP features.

}, issn = {0003682X}, doi = {10.1016/j.apacoust.2017.01.025}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0003682X17300774}, author = {Esfahanian, Mahdi and Erdol, Nurgun and Gerstein, Edmund and Zhuang, Hanqi} }