speakers identification system for core networks using hadoop
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SPEAKERS IDENTIFICATION
SYSTEM FOR CORE NETWORKSUSING HADOOP CLUSTERS
PROJECT SUPERVISOR:
DR.SHOAB A.KHAN
GROUP MEMBERS:
AMSAL NAEEM
TAHREEM KHALID
RAHEEL MUMTAZ
DE 30 (CE)
COLLEGE OF E&ME, NUST
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OUTLINEO Motivation & problem definition
O System level design
O AlgorithmsO Implementation
O Results
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MOTIVATIONO Security
O Terrorism
O Keep a check on important peopleO Political issues
O Cricket
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Terrorist Incidents
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Match Fixing in Cricket
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PROBLEM SOLUTIONDesign a system that can
O Process large number of calls at a time
O Process large data setsO Identify the person an a particular call
O Monitor the communication without
interruption
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SYSTEM LEVEL DESIGNO Components
O GUI explanation
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Recoding FeatureExtraction
Matching
Database
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Graphical User Interface
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Graphical User Interface
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Design And ImplementationO Training
O Testing
O Hadoop architectureO Hbase architecture
O Speaker identification
O Mfcc
O Vector quantization
O Matching (distance measurement)
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IN N
IN 1 IN 2
OUTPUT
HADOOP MASTER
MASTERTaskTracker
SLAVE 1TaskTracker
SLAVE 2TaskTracker
SLAVE 3TaskTracker
SLAVE 4TaskTracker
MAP MAP MAP MAP MAP
SORT SORT SORT SORT SORT
MERGE
REDUCE
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TRAINING
O The voice samples are
recorded in Matlab
O MFCC Features are
extracted from the voice
input.
O Vector Quantization
using K-Means is done
to reduce feature
vectors.
O Insert above feature
vectors to HBase table.
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TESTINGO Input recordedO MFCC Features are
extracted
O Features input to
HADOOP Cluster.
O Distributed inputs to allmachines
O MapReduce tasks on
each TaskTracker.
O Euclidean distance is
measured
O The output consists of
the most likely matched
speaker.
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HADOOP ARCHITECTURE
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MAPREDUCE WORKING
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HBASE ARCHITECTURE
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Speak
er 1
Speak
er 2
Speak
er N
UnknownSpeaker
FEATUREEXTRACTION
SPEAKER
MODELLING
MATCHING
FEATURE
EXTRACTION
DATABASESPEAKER
IDENTIFIED
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Mel Frequency Cepstral
CoefficientsSamplin
g
Log
Mel-Frequenc
yWarping
InverseDFT
Framing &Windowin
g
DiscreteFourierTransfor
m
Mel
Cepstru
m
Voice
Signal
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MFCCO
Hamming Window
where 0 n N-1
N=length of frame
O Discrete Fourier Transform
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MFCC
O Mel-Frequency warping
O Inverse Discrete Fourier Transform
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VECTOR QUANTIZATION
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NO
K-Means Clustering
Select K objects randomly from M data objectsto take as initial clustering centers
Assign all data object to its nearest cluster center
Update each center by averaging all of the points
that have been assigned to it
Stop
Have centroids
changed?
YES
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MATCHINGEuclidean Distance
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RESULTSO Graphs
O Comparisons
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COMPARISON
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COMPARISON
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COMPARISON
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RESULTS
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CONCLUSIONO Summary of project
O Summary of results
O Future extension
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