GENERAL CONCEPT OF THE ACOUSTICAL AVIAN MONITORING SYSTEM R. Wielgat (1), A. Lisowska-Lis (1), T. Potempa (1), K. Walasz (2), D. Wiehle (3), P. Kozioł (4), D. Król (1) Department of Technology, State Higher Vocational School in Tarnów, (2) Institute of Environmental Sciences, Jagiellonian University in Kraków (3) Department of Zoology and Ecology, University of Agriculture in Kraków (4) Complex of Foothills Landscape Parks in Tarnów

Reasons for avian monitoring Maintenance of biodiversity. Detection of ecological disasters. Monitoring actions in national and landscape parks Monitoring of environmental quality (Farmland Bird Index – FBI) Protection of endangered bird species Protection of plants in agriculture Protection of airports and planes

Problems of avian monitoring actions In order to monitor bird species there are organized monitoring programs involving large number of volunteers and skilled experts. In some actions necessary is catching wild birds (stressful). Collecting and analyzing results of observations are often difficult and troublesome. Mistakes are difficult to point out and correct.

General concept of the monitoring system Automatic Observer Bird voice recognizer in unsupervised mode Stationary digital recorder Guest Mobile digital recorder Expert or Administrator Information system GPS, movies, photos, weather information Stationary digital recorder Mobile digital recorder Observer Bird voice recognizer in supervised mode

Transects and observation points In order to experimentally evaluate all the system 6 transect and 7 observation points were chosen in Malopolska region, especially in Complex of Foothills Landscape Parks in Tarnów. Transects and observation points were defined, to represent variety of ecosystems: forests, parks, meadows, ponds, lakes, river bands, municipal terrains and refuse dump. More than 30 species vocalizations would be analysed during 3 years. „rezerwat Styr” transect „Zbylitowska Góra” observation point

Conclusion The general concept of the acoustical avian monitoring system has been presented. Preliminary Bird voice recognition experiments involving MFCC and HFCC features as well as DTW classification method have been carried out. Results of the experiments are promising to implement Bird Voice Recognizer. 6 transects and 7 observation points have been determined along which initial test observations and recordings have been done. Preliminary web site version presenting encyclopaedic information on bird species has been prepared.

Future work and research Future research and work in years 2008 - 2011 will include: Recognition experiments using TDSC, wavelet, spectral peaks features and HMM classification method. Implementation Bird Voice Recognizer as a computer program. Hardware implementation of digital recorder and bird attracting device. Implementation of data base and web site together with expert system.

Lanius corullio L. – red-backed shrike Acknowledgment Described work is financed from grant of Polish Ministry of Science and Higher Education number N N519 402934. Lanius corullio L. – red-backed shrike

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Stationary Digital Recorder LCD Keyboard Wireless Transceiver Antenna Real Time Clock MICROCONTROLLER FAT32 Broadbandcondenser microphone Memory Card ADC DAC Microphone amplifier Power amplifier Alluring speakers Return

Mobile Digital Recorder LCD Keyboard GPS Antenna Real Time Clock MICROCONTROLLER FAT32 Broadbandcondenser microphone Memory Card Headphones ADC DAC Microphone amplifier Headphones amplifier Return

Bird Voice Recognizer – Unsupervised Mode Bird Voice Recognizer is a computer program capable to recognize bird species automatically using formerly recorded voice of the recognized bird species. Bird voice recognition is usually performed in the following stages: feature extraction classification Bird voice recognition in unsupervised mode can be enhanced by an expert system using additional information like weather forecast, date and hour of the recordings, GPS position which are registered simultaneously with recognized bird voice. Return

Feature Extraction There are various features which can be extracted from bird voice signal for instance: TDSC (Time Domain Signal Coding) , spectral peaks, wavelets, MFCC (Mel Frequency Cepstral Coefficients), HFCC (Human Factor Cepstral Coefficients). Feature extraction in automatic bird voice recognition is sometimes preceded by initial signal processing like bandpass filtration, noise cancelation etc. So far MFCC and HFCC features were tested in the experiments obtainig promising ca. 92% recognition accuracy in the closed set experiment. Return

Classification The most promising classification methods in bird voice recognition is Dynamic Time Warping (DTW) based word spotting and Hidden Markov Models (HMM) method. iY iX BIRD VOICE X BIRD VOICE Y 1 N M 2 3 4 5 1 o1 o2 o3 o4 o5 o6 a23 a22 b2(o1) b2(o2) b2(o3) b4(o5) b3(o4) b4(o6) a34 a45 a12 a33 a44 HMM DTW Return

Bird Voice Recognizer – Supervised Mode In the supervised mode bird species recognized by Bird Voice Recognizer can be initially verified by the observer entering data to the system. The observer besides synchronized in time automatically captured information like bird voice, weather information, time and date, GPS position can also provide additional information like photos, movies and description of the bird or its behavior. Provided information together with initial observer verification can help in further bird species verification by an expert. Return

Information system Information system (IS) will consist of: Operating system (Linux distribution) Object-relational database (PostgreSQL); Data Warehouse supporting OLAP functions, homogeneously cooperating with database; Application server; IS will be bulit in three-tier architecture using MVC (Model – View – Controller) pattern. In order to store huge amount of multimedia data server will be equipped with almost 10 TB HDDs which are supervised by specialized RAID controller.

Database Server with installed relational database will be a storage of data collected as a result of bird species recordings and observation. Database will contain various types of data especially audio files with bird voices, movies, photos, descriptions of particular bird which will be related to each other. Database will be divided into two parts: encyclopaedic one and experimental one. Return

Roles – Guest Guest will have authorization for: browsing encyclopaedic information about bird species; adding bird voices in order to recognize bird species automatically; Return

Roles – Observer Observer will have authorization for: adding results of observations; modyfing its own observations; all actions which are covered by guest role; Observer Return

Roles – Expert and Administrator Expert or Administrator Expert will have authorization for: verifying information provided by observers or an automatic-observer; recognition questionable bird voices; Making statistical analysis supported by data mining granting role of observer; all actions which are covered by observers role; Administrator will have authorization for: granting role of expert; all actions which are covered by an expert role; Return