Craig HolmesBrad Klippstein

Andrew PottkotterDustin 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

))(

)()(

(2

22

2

NY

YNX

X

NYX

XYr

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???