Time frequency analysis of bird’s chirps new
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Transcript of Time frequency analysis of bird’s chirps new
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ime frequency nalysis of bird schirps. . . . .H P A A Abewardana
S9149/ /007 s 10522
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Outline In tro d u ctio n
Background
Objectives
Methodology
References
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Introduction N e a rly a ll b ird s p ro d u ce so m e so rt o f vo cal so u n d
w h ich is u sed in co m m u n ica tio n w ith th e ir o w n.sp e cie s a n d a lso b e tw e e n d iffe re n t sp e cie s
, W ith in d iffe re n t p o p u la tio n s o f th e sam e sp e cie s
.th e so n g is le ss va ria b le .T h e re a re d iffe re n t call typ e s o f e a ch b ird M ost o f
.( . .th e m h a ve b e tw e e n 5 to 6 d istin ct call typ e s e gT h e re a re id e n tifie d 1 3 d iffe re n t co m m on ca ll
,typ es of th e S ri Lan ka M ag p ie 1 1 call typ es of..)b la ck b ird e tc
A b ird so u n d cla ssifica tio n syste m is ve ry u se fu l to.stu d y th e b e h a vio r o f th e b ird s
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Background
M an y resea rche s h av e b e en carried ou t to stu d yth e va ria tio n a m o n g th e b ird ca lls
S om e stu d ies h a ve con d u cted am o n g d iffe re n cekin d s b ird typ e s to id e n tify th e in h a rm o n ic a n d
.tra n sie n t sou n d s
Th e result of m ost of th ese stu d ies w ere verye n co u ra g in g a n d in d ica te th a t it is p o ssib le to
re co g n ize b ird sou n d s o f th e te st sp e cie s u sin g.w a ve le t tra n sfo rm a tio n a n d n e u ra l n e tw o rks
S tu d y o f re co g n izin g th e h a rm o n ic stru ctu re o f th eb ird vo caliza tio n h a s b e e n carrie d o u t u sin g
n u m be r of m eth od s su ch as p aram etric lin e,sp e ctru m e m issio n m e th o d sylla b le h isto g ra m s
..e tc
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Objective a in O b je ctiv e D ev elop a tech n iq u e for recog n ition of b ird
.sp e cie s b y th e ir sou n d s
pecific Objectives Study whether we can identify the different kinds
of birds that live in a specific region based ontheir voice
Study whether there is a variation of the sound.among the individuals in a flock of birds Considering the availability of data Sri Lankan
magpie is selected for the study
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Study the variation of the sound of the similar kindof birds that live in the wet and dry zones in Sri.Lanka
There are some species that mimic the calls of other. species for many reasons Identify whether there s
a differences between the original and the imitate.( )sound Drongo
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Methodology ata Acquisition
The sound samples of various kinds of birds are,provided by the Department of Zoology
.University of Colombo The data was collected from the Sinharaja Natural
( ; ) World Heritage Site 6 21 26 N 80 21 24 E.in Sri Lanka
Sample size 50 sound tracks for each bird.
Considering the feasibility and the quality of theresults of the neural network sample size of 50
is selected.
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Method
The noise is removed using a suitable filter from thesound tracks.
Wavelet transformation is using to obtain andanalysis the audio spectrogram of each bird and
specify the variables. (frequency, distancebetween the peaks, avg. distance between thetwo notes etc. )
Using the suitable variables constructing anunsupervised neural network
During the training phase the network is beingtrained using the obtained sound samples.
Finally the system is testing on separate testingdata
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ime PlanTask Oct Nov Dec Jan Feb Mar Apr May Jun Jul
. iterature Review
. ata acquisition& sorting
. &btaining the waveletemoving the noise of theound samples. dentifying the variables
. onstructing the neuralnetwork
. raining the network
. esting the network
. inal report
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References -Research Article Wavelets in Recognition of Bird
Sounds [Department of Information Technology, Tampere
University of Technology, Pori, Finland]
Sensor Network for the Monitoring of Ecosystem:Bird Species Recognition [Microsoft-QUT eResearch Centre, Faculty of Information
Technology, Queensland University of Technology, Brisbane,QLD 4001, Australia]
Bird song recognition based on syllable pairhistograms
[Panu Somervuo, Neural Networks Research Centre,Helsinki University of Technology, Espoo, Finland]
[Aki Harmay, Laboratory of Acoustics and Audio Signal
Processing, Helsinki University of Technology Espoo,Finland]
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Classification of the harmonic structure in birdvocalization
[Panu Somervuo, Neural Networks Research Centre,Helsinki University of Technology, Espoo, Finland]
[Aki Harmay, Laboratory of Acoustics and Audio SignalProcessing, Helsinki University of Technology Espoo,Finland]
Two sympatric species of passerine birds imitatethe same raptor calls in alarm contexts.
[C. P. Ratnayake : E. Goodale (*) : S. W. Kotagama Field Ornithology Group of Sri Lanka, Department of
Zoology, Faculty of Science, University of Colombo, Colombo 3, Sri Lanka]
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!!hank You