1 detection and discrimination of sniffing and panting sounds of dogs ophir azulai(1), gil bloch(1),...
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Detection and Discrimination of Sniffing and Panting Sounds of Dogs
Ophir Azulai(1), Gil Bloch(1), Yizhar Lavner (1,2), Irit Gazit (3) and Joseph Terkel (3).
1. Signals and Image Processing Lab (SIPL), Faculty of Electrical Engineering, Technion IIT, Haifa, Israel (http://www-sipl.technion.ac.il)
2. Computer Science, Tel-Hai Academic College, Upper Galilee, Israel.3. Department of Zoology, George S. Wise Faculty of Life Sciences Tel-Aviv
University, Tel Aviv 69978, Israel.
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Abstract
Dogs’ acute sense of smell enables them to accurately identify many types of odors. This ability serves in the detection of drugs, explosives, finding people, and other special tasks. The possibility of controlling dogs’ behavior suggests many potential applications.For this purpose, an automatic system for recognition of panting and sniffing according to their corresponding acoustic signals was developed. The system consists of three main components: analysis, training and discrimination. In the analysis stage the events were detected using short-time energy and zero-crossing rate functions. Features for discrimination were extracted utilizing an autoregressive model or by means of a Mel-frequency cepstrum. A few parameters of the spectral envelope were selected, based on their discrimination ability. In the training stage, a feedforward neural network was trained, using labeled signals. Alternatively, a fuzzy K-nearest neighbor algorithm can be used. Correct identification of up to 96.8% was achieved in the discrimination stage.
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Panting and Sniffing Signals
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Discrimination Method 1
Identify the calibrationevents using
short time energyfunction
Train a multi-layerneural network .
Calibration Process Analyse Process
Extract AR modelfrom each event
Extract AR modelcoefficients from
each event
Extractpre-defined
parameters fromAR frequency
response
Identify the events usingshort time energy
function
Extract AR modelfrom each event
Extract AR modelcoefficients from
each event
Present theparameters to the
trained neuralnetwork
Extractpre-defined
parameters fromAR frequency
response
Use a pre-defineddecision rule
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Events finding
Using Normalized Short Time Energy Function
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AR model frequency response
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Neural Network
Using three layers, feed forward network with back propagation learning method
Inpu
t V
ecto
r
Input Layer
Hidden Layer
Output Layer
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Discrimination Method 2
Calculate theMFCC's of the
signal
Calculate theEFV's of the
signal
Classificationusing fuzzy
KNN
Decision rulefor eventlocation
Analyze Process
Identify the calibration
events
Extract MFCC'sfrom each
event
Find the mostseparable
MFCCcoefficient
Create theEFV's fromeach event
Calibration Process
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Mel-Frequency Cepstral Coefficients
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Event Feature Vector
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Fuzzy KNN
Y-A
xis
X-Axis
AnalyzedFeature Vector
PantingFeatureVectors
SniffingFeatureVectors
k
j
m
j
k
j
m
jij
i
xx
xxU
xU
1
)1/(2
1
)1/(2
/1
/1
Fuzzy KNN with K=3
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Membership FunctionClassification results using Fuzzy KNN
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Comparison of both methods
#PhaseMethod 1Method 2
1.Event findingUsing short time energy function
Manually
2.Calculation of basic feature vector
AR model frequency response
MFCC
3.Parameters extraction
ManuallyMost Separable
4.Feature vectorExtracted parameters
EFV
5. CalibrationNeural Network Training
-
Calibration Process:
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Comparison of both methods continued
#PhaseMethod 1Method 2
1.Event findingUsing short time energy function
-
2.Calculation of basic feature vector
AR model frequency response
MFCC
3.Feature vectorExtracted parameters
EFV
4.ClassificationNeural NetworkFuzzy KNN
5.Decision rulePre-defined threshold
Not yet defined
Analyze Process:
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Conclusions
1. Sniffing and Panting sounds has unique signature that can discriminate between them
2. Some frequency response model can give us the desired information.
3. Event finding should combine both feature vector and some other method like energy function or ZCR.