1. „B I R D S M O N D” BIRD SOUND MONITORING DATABASE
Robert Wielgat, Agnieszka Lisowska-Lis, Tomasz Potempa, Daniel Król
Department of Technology, State Higher Vocational School in Tarnów,
Paweł Kozioł
Complex of Foothills Landscape Parks in Tarnów
Zbigniew Bonczar, Damian Wiehle, Marcin Lis
Department of Zoology and Ecology, University of Agriculture in Kraków
Kazimierz Walasz
Institute of Environmental Sciences, Jagiellonian University in Kraków
Antoni Ligęza, Tomasz Zieliński
AGH University of Science and Technology in Kraków

2. 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

3. 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.

4. Birdsmond Project Objectives
The following task to accomplish are planned in the years
Building recording device capable to record sounds and ultrasounds from several microphones and capable to measure GPS position.
Building bird attracting device
Writing a program for automatic recognition of bird voices
Implementing the data base accessed via Internet capable to store multimedial data coming from bird monitoring actions.
Evaluating the system during monitoring actions and scientific expeditions.

5. 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

6. Monitored Area
Monitored Area Includes Małopolska voivodeship in the south-east Poland especially terrain of Complex of Foothills Landscape Parks in the vicinity of Tarnów

7. 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, swamp, lakes, river bands, municipal terrains and refuse dump. More than 30 species vocalizations would be analysed during 3 years.

8. Transects and observation points
„Krzyskie” ponds
Zbylitowska Góra
Mydlniki
Tarnów
PWSZ
Tarnowiec Radlna
Polichty
„Styr” reservation
„Stony Town” - Ciężkowice
„Rożnowskie” Lake
- Observation points
- Transects

9. Transect: Tarnowiec-Radlna
Satelite picture of Tarnowiec-Radlna transect
Tarnowiec-Radlna transect includes farmland, river banks and small village areas.

10. Transect: „Styr” Reservation
„Styr” Reservation Transect includes mainly forest areas

11. Transect: „Stony Town” -Ciężkowice
„Stony Town” Ciężkowice Transect includes forest and small rocks areas

12. Transect: „Krzyskie” - Ponds
„Krzyskie” Ponds Transect includes ponds and swamp areas

13. Polichty Transect includes mainly forest areas and meadows
Transect: Polichty
Polichty Transect includes mainly forest areas and meadows

14. Transect: Rożnowskie Lake
Satelite picture of Rożnowskie Lake transect
„Rożnowskie” Lake Transect includes artifical lake and forest areas

15. Observation Point: Zbylitowska Góra
Zbylitowska Góra Observation Point is located on the Dunajec river bank near Collared Sand Martin (lat. Riparia riparia, Ital. topino) colony

16. Observation Point: PWSZ Tarnów
Satelite picture of PWSZ Tarnów observation point
PWSZ Tarnów observation point is located in the municipal region

17. Observation Point: Mydlniki
Mydlniki observation point is located in the experimental breeding farm of Peregrine Falcon (Lat. Falco peregrinus) (Ital. falco peregrino)

18. 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.

19. Future work and research
Future research and work in years 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.

20. Lanius collurio – red-backed shrike - L'averla piccola
Acknowledgment
Described work is financed from grant of Polish Ministry of Science and Higher Education number N N
Lanius collurio – red-backed shrike - L'averla piccola

21. THANK YOU VERY MUCH FOR YOUR ATTENTION
Emberizza citrinella – Yellowhammer
- Lo zigolo giallo
Garrulus glandarius – Jay
- La ghiandaia
Emberizza citrinella – Yellowhammer
- Lo zigolo giallo
THANK YOU VERY MUCH FOR YOUR ATTENTION
For more information visit project website:

22. Stationary Digital Recorder
LCD
Keyboard
Wireless
Transceiver
Antenna
Real
Time
Clock
MICROCONTROLLER
FAT32
Broadbandcondenser
Microphone x 4
Memory
Card
ADC
DAC
Microphone
Amplifier x 4
Power
amplifier
Alluring
speakers
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23. Mobile Digital Recorder
LCD
Keyboard
GPS
Antenna
Real
Time
Clock
MICROCONTROLLER
FAT32
Broadbandcondenser
microphone x 4
Memory
Card
Headphones
ADC
DAC
Microphone
Amplifier x 4
Headphones
amplifier
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24. 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.
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25. 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.
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26. 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
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27. 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.
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28. 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.

29. 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.
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30. Roles – Guest Guest will have authorization for:
browsing encyclopaedic information about bird species;
adding bird voices in order to recognize bird species automatically;
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31. Roles – Observer Observer will have authorization for:
adding results of observations;
modyfing its own observations;
all actions which are covered by guest role;
Observer
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32. 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;
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