Towards the Automatic Acoustical Avian Monitoring System

Eurasip Journal on Advances in Signal Processing, 2006

This paper presents a novel bird monitoring and recognition system in noisy environments. The project objective is to avoid bird strikes to aircraft. First, a cost-effective microphone dish concept (microphone array with many concentric rings) is presented that can provide directional and accurate acquisition of bird sounds and can simultaneously pick up bird sounds from different directions. Second, direction-of-arrival (DOA) and beamforming algorithms have been developed for the circular array. Third, an efficient recognition algorithm is proposed which uses Gaussian mixture models (GMMs). The overall system is suitable for monitoring and recognition for a large number of birds. Fourth, a hardware prototype has been built and initial experiments demonstrated that the array can acquire and classify birds accurately.

The 6th International Conference on Interdisciplinarity in Education ICIE’11 April 14-16, 2011, Karabuk/Safranbolu, TURKEY, 2011

The system which can monitor bird species based on automatic bird voices recognition is very useful in order to protect biodiversity of avifauna. In the paper main aspects of using web mapping services in distributed environment together with database management system are described. Furthermore, architecture of the subsystem responsible for storing, managing and exchanging geospatial data with web mapping interfaces is proposed. Also some examplary results of querying geospatial information from the Acoustical Bird Monitoring System (ABMS) and external GIS servers are presented. GIS in the acoustical bird monitoring system.

IRJET, 2023

Now a days bird population is changing drastically because lots of reasons such as human intervention, climate change, global warming, forest fires or deforestation, etc., With the help of automatic bird species detection using machine learning algorithms, it is now possible to keep a watch on the population of birds as well as their behavior. Because manual identification of different bird species takes a lot of time and effort, an automatic bird identification system that does not require physical intervention is developed in this work. To achieve this objective, Convolutional Neural Network is used as compared to traditionally used classifiers such as SVM, Random Forest, SMACPY. The foremost goal is to identify the bird species using the dataset including vocals of the different birds. The input dataset will be pre-processed, which will comprise framing, silence removal, reconstruction, and then a spectrogram will be constructed, which will be sent to a convolutional neural network as an input, followed by CNN modification, testing, and classification. The result is compared with pre-trained data and output is generated and birds are classified according to their features (size, colour, species, etc.

Applied Acoustics, 2014

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 researchers, conservation practitioners, and decision makers, such as environmental indicator taxa and threatened 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 songbird) and the Common Kingfisher Alcedo atthis, a non-passerine species with a wide distribution throughout 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.

2010

Trends in bird population sizes are an important indicator in nature conservation but measuring such sizes is a very difficult, labour intensive process. Enormous progress in audio signal processing and pattern recognition in recent years makes it possible to incorporate automated methods into the detection of bird vocalisations. These methods can be employed to support the census of population sizes. We report about a study testing the feasibility of bird monitoring supported by automatic bird song detection. In particular, we describe novel algorithms for the detection of the vocalisations of two endangered bird species and show how these can be used in automatic habitat mapping. These methods are based on detecting temporal patterns in a given frequency band typical for the species. Special effort is put into the suppression of the noise present in real-world audio scenes. Our results show that even in real-world recording conditions high recognition rates with a tolerable rate of false positive detections are possible.

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004

… , 2006. ICPR 2006. 18th …, 2006

reduces the limitations of traditional recognition facilitating the ornithologist's work and improving the quality of their research. This allows the experts to focus just on high level bird behavior interpretation, working either directly from their labs or even through the Internet. For this work, the goal of sensor networks [4, 8, 9] is to introduce a certain number of small sensors or motes in a natural environment in order to acquire data from their surroundings, without human intervention. The large amount of data collected this way demands the use of sophisticated computational tools for their processing. The work reported in this paper is part of collaboration between UCLA and ITESM in the ongoing project "Sensor Arrays for Acoustic Monitoring of Bird Behavior and Diversity" [9] whose specific goal is to monitor different bird species from the ecological reserves in California, USA and Chiapas, Mexico. 2. Methods 2.1. The Bird Songs Bird Songs for this study were obtained from the Cornell Lab of Ornithology, Macaulay Library [3]. Songs from three species were provided: great antshrike, Taraba major (49 song files); dusky antbird, Cercomacra tyrannina (79 song files); and barred antshrike, Thamnophilus doliatus (76 song files). Each song file has from a few seconds to several minutes of bird calls, with either one, two or more birds singing simultaneously. The reason to choose these species is because they are abundant in Montes Azules, Chiapas, an ecological reserve where the sensor network will be deployed in the near future. 2.2. Feature Extraction The study of bird species can be improved, thus we need to use software tools to extract the different features of the signal which will be used later on to analyze and interpret the sound using computers. Once we obtained the songs in .wav format, we loaded them into Sound Ruler [7]. With this software, we are able

2012 IEEE 24th International Conference on Tools with Artificial Intelligence, 2012

We report on a recent progress with the development of an automated bioacoustic bird recognizer, which is part of a long-term project , aiming at the establishment of an automated biodiversity monitoring system at the Hymettus Mountain near Athens. In particular, employing a classical audio processing strategy, which has been proved quite successful in various audio recognition applications, we evaluate the appropriateness of six classifiers on the bird species recognition task. In the experimental evaluation of the acoustic bird recognizer, we made use of real-field audio recordings of two bird species, which are known to be present at the Hymettus Mountain. Encouraging recognition accuracy was obtained on the real-field data, and further experiments with additive noise demonstrated significant noise robustness in low SNR conditions.

IFAC-PapersOnLine

Recently, more attention is being payed to the ecological environment due to the rapid decline of animal species, especially birds. Wireless Multimedia Sensor Networks (WMSN) can be leveraged to monitor, protect and assess changes in bird populations by periodically capturing and sending images to a collect station. In this paper, we propose a three-tier architecture following the Cloud-Fog-Edge model with an adequate placement of the surveillance system tasks. To address the sensor network limited resources, we propose to reduce the amount of visual data to be reported by sending compressed region of interest (ROI) of only target species images. Endangered birds are identified based on their calls before triggering the camera. However, audio recognition is likely to fail due to the ambient noise that can be encountered in a natural environment. As a result, we augmented our automatic bird song recognition system with an appropriate noise reduction technique. Thanks to the efficient cooperation of its three tiers, the proposed system, based on low-complexity audiovisual information processing, results in a better use of network resources while maximizing the amount of pertinent information.