craig holmes brad klippstein andrew pottkotter dustin osborn
TRANSCRIPT
Craig Holmes
Brad Klippstein
Andrew Pottkotter
Dustin Osborn
Bird and Bat Call Embedded Recognition System
Purpose•Wind Turbines have become a great source of alternative “green” energy
•Lake Erie and North West Ohio have the potential to be a great source of wind energy.
•North West Ohio also happens to be one of the largest migratory bird fly-ways in the country
•Before wind turbines can be installed questions need to be answered about the effect they will have on the avian population in the area
“As wind energy facilities becomes substantially more numerous and as wind development continues to grow, fatalities and thus the potential for biologically significant impacts to local populations increases” –National Wind Coordinating Collaborative
The BBCER strives to create a way to identify what avian species are being impacted by the wind turbines and to allow for conclusions to be made about what effect they are having on the species.
Purpose
BBCER
ComponentsBird/Bat Calls Embedded Recognition
SystemBird System Bat System
Hardware10 bit ATMega 328 A/D microcontroller AR125 Ultrasonic Receiver
Microphones Stand FR125-III Field Recorder
Four UEM-88 Mini Shotgun Microphones
MAYA-44
PC
SoftwareBird Call Library SPECT'R Software
Raven-X SCAN'R Software
Matlab SonoBat Software
Bird Recognition Side of System
Recognition AlgorithmIncoming Unknown Bird Call
Take First 512 Data
Points from Call
Apply Hamming Window to Data Points
Derive Fourier
Transform of Data Points
Find the Magnitude of the Fourier Transform
Multiply by the Mel
Frequency Scale
Gather Mel Frequency Cepstral
Coefficients
Apply Discrete Cosine
Transfrom to MFCC's
Place First 9 Coefficients
in MFCC Bank
Move to Next 512 Data
Points from Call
Once through all 220,500 Data Points of Call, Send
MFCC Bank to Correlation Algorithm for Matching
“Scale of pitches judged by listeners to be equal in distance from one another”[1]
Converts Hertz scale to Mel scale.
Mel Scale
)1700(log*2595 10 f
Cepstral: Fourier Transform of the log spectrum. [1]
Mel Frequency Cepstrum (MFC): Representation of the short term power spectrum of sound, based on a linear cosine transform of a log power spectrum on a nonlinear Mel scale of frequency.[1]
Mel Frequency Cepstral Coefficients (MFCC): Coefficients that collectively make up an MFC[1]
Mel Frequency Cepstral Coefficients
Original Call
MFCC’s
Correlation Algorithm
Incoming Unknown
MFCC Bank
Calculate Pearson's
Correlation Coefficient with first
database call
Calculate Next Pearson's
Correlation Coefficient for
next known call
Repeat calculating correlation
coefficient for all database
calls
Output call with highest
correlation as best match
If all calculated correlation
coefficients below threshold then unknown call
Measures the correlation between two linear dependent variables.
Signified by r (rho) and can take on values between -1 to 1. Where -1 signifies perfect negative correlation, 0 is no correlation, and 1 is perfect positive correlation.
The previous example of American Crow and American Coot yield an r value of 0.4650
Pearson’s Correlation Coefficient
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With our system we are able to successfully identify an ‘unknown’ bird call that has been recorded through the UEM-88 mini-shotgun microphones and interfaced to the PC with the MAYA-44 USB device.
Bird Results
Results Continued…
Correlation Coefficient of .6411
Bat Recognition Side
Together the AR125 and FR125-III record the ultrasonic bat calls from the field.
They then work with the SPECT’R, SCAN’R, and SonoBat software to determine the species of which the call came from.
The system has been tested and has successfully identified species of bats in the field.
Bat Recognition
There are three software programs that we purchased for bat call analysisSPECT’RSCAN’RSonoBat.
SPECT’R is used to perform spectral analysis, digital tuning, and hard-disk recording of echolocation calls.
SCAN’R is a snapshot characterization and analysis tool which is used to distinguish between bat calls and unwanted noise.
SonoBat can be used to analyze and compare high-resolution full-spectrum sonograms of echolocation calls.
Bat Recognition
Because of the Microcontroller, that we purchased, memory limitations , we are unable to successfully record an audio call with enough data points to be ran through our algorithm.
In the future we recommend purchasing a microcontroller with at least 512kB SRAM to get a correctly sampled call plus room to store the code.
We also recommend making the system wireless so that the system could be placed in the field and left to record bird calls overnight or for long durations.
Future Recommendations
Questions???