speakers identification system for core networks using hadoop

7/31/2019 Speakers Identification System for Core Networks Using Hadoop

1/31

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


2/31

OUTLINEO Motivation & problem definition

O System level design

O AlgorithmsO Implementation

O Results


3/31

MOTIVATIONO Security

O Terrorism

O Keep a check on important peopleO Political issues

O Cricket


4/31

Terrorist Incidents


5/31

Match Fixing in Cricket


6/31

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


7/31

SYSTEM LEVEL DESIGNO Components

O GUI explanation


8/31


9/31

Recoding FeatureExtraction

Matching

Database


10/31

Graphical User Interface


11/31

Graphical User Interface


12/31

Design And ImplementationO Training

O Testing

O Hadoop architectureO Hbase architecture

O Speaker identification

O Mfcc

O Vector quantization

O Matching (distance measurement)


13/31

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


14/31

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.


15/31

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.


16/31

HADOOP ARCHITECTURE


17/31

MAPREDUCE WORKING


18/31

HBASE ARCHITECTURE


19/31

Speak

er 1

Speak

er 2

Speak

er N

UnknownSpeaker

FEATUREEXTRACTION

SPEAKER

MODELLING

MATCHING

FEATURE

EXTRACTION

DATABASESPEAKER

IDENTIFIED


20/31

Mel Frequency Cepstral

CoefficientsSamplin

g

Log

Mel-Frequenc

yWarping

InverseDFT

Framing &Windowin

g

DiscreteFourierTransfor

m

Mel

Cepstru

m

Voice

Signal


21/31

MFCCO

Hamming Window

where 0 n N-1

N=length of frame

O Discrete Fourier Transform


22/31

MFCC

O Mel-Frequency warping

O Inverse Discrete Fourier Transform


23/31

VECTOR QUANTIZATION


24/31

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


25/31

MATCHINGEuclidean Distance


26/31

RESULTSO Graphs

O Comparisons


27/31

COMPARISON


28/31

COMPARISON


29/31

COMPARISON


30/31

RESULTS


31/31

CONCLUSIONO Summary of project

O Summary of results

O Future extension

speakers identification system for core networks using hadoop

Documents