fyp final report_robotic arm

DEDICATION

We dedicate this project to our

beloved, caring, loving and respected

Parents

And

Family members.

I

ACKNOWLEDGEMENTS

With the Grace of the Almighty ALLAH WHO bestowed us a lot of blessings

and we became able to complete our project, we are thankful to ALLAH who

is the Creator of the whole universe.

We acknowledge the help and support of our parents and teachers, due to

which we have reached our destination. We are thankful to our supervisor sir

Wajahat for encouraging us during our project.

II

ABSTRACT

Making the machine to learn to recognize the human voice is a great need of

the day. Voice recognition is the process of making the computer intelligent

enough to distinguish a voice spoken by a human from all other voices. In

voice recognition voice is first recorded sampled, framed, windowed and then

different features which make it distinguishable are extracted. These features

include short time energy, zero crossing rate, and spectral roll off spectral flux

spectral centriod. Once these features are extracted then pattern recognition is

applied whose output moves the robotic hand according to the voice command

of the user. Neural networks are used for pattern recognition with five neurons

at the input layer. This voice recognition system is 98 percent accurate when

experimented with a single user. It covers all the commands needed to control

a robotic arm.

III

Table of Contents

Dedication............…………………..……………………………………..…..I

Acknowledgements………………………..…………………………………II

Abstract …………………………………..………………………………... III

Table of Contents……………...…………………………………………....IV

List of Figures……………………………………………………………..VIII

List of Tables ……………………………………………………………. ....X

Chapter 1: Introduction….………………………………………………..…1

1.1 Problem statement................................................................................1

1.2 Objective…………..…………………...……………………........….1

1.3 Organization of project report..............................................................2

Chapter 2: Literature Review ...…………………………………………….3

2.1 Aim and Purpose……………………………….…………….............3

2.2 Mechanical Assembly Review……..……………...............…............4

2.3 Speech Recognition Review….…........................................................5

Chapter 3: Design Procedure………………………………………………..6

3.1 Artificial Intelligence………………….……………….……….....….6

3.2 Machine Learning………………………………………….................6

3.2.1 Definition………………………………………..……………..7

3.2.2 Generalization…………,…………...……………………….…7

3.3 Human Interaction………….………..……………................…….… 7

3.4 Algorithm types…………...………......................................................7

IV

3.4.1 Supervised learning……………………………………… ……8

3.4.2 Statistical classification…………………………….…………..9

3.5 Artificial neural network………................................................................10

3.5.1 ANN in Matlab………..………………………………………10

3.5.2 How does ANN work…………….………..………….………12

3.6 Transfer functions………….………………………………….…..….…..13

3.6.1 Hard-limit transfer function ………………….………………13

3.6.2 Linear transfer function ……………………....………………14

3.6.3 Sigmoid transfer function ……………………………….……14

3.6.4 Feed-forward back propagation…………………………….…15

3.6.5 Epoch……….………………………..…………………….….17

3.6.6 Training of ANN……………………………………..…….…17

3.7 Mechanical Assembly………………………………………………....…17

3.7.1 Types of industrial robots……………..………………...….…18

3.7.2 Components..……………………...……………..................…18

3.8 Mechanical construction………………..………………………….…..…19

3.9 Characteristics of cyborg arm……………………………..……………...20

3.10 Finger construction with spring………..………………………..............21

3.10.1 How to grip…….…………………………....…………….…21

Chapter 4: Design Details………………………………………….……….22

4.1 Introduction…………………………………………..…………………..22

4.2 First phase…………………………………….……………….

………….22

4.2.1 Sampling rate ………………………………………………...22

V

4.2.2 Recording…………………………………………………..…23

4.2.3 Retrieving …………………………………………...……..…23

4.2.4 Normalizing………………………………………..................24

4.2.5 Framing…………………………………………….................24

4.2.6 Windowing…………………………………........…………....24

4.3 Feature extraction….....……………………………………………..……27

4.4.Features……………………………………...………………………...…27

4.4.1.Zero crossings rate………………...…………….................…28

4.4.2 Short time energy………………………..........................……30

4.4.3 Spectral roll off…………………………………..……..…….31

4.4.4 Spectral flux………………………………………….....….....32

4.4.5 Spectral centroid………………………………………….... ..32

4.5 Electrical and electronic components…………………………………….32

4.5.1 DC power supply…………..…………………………………33

4.5.2 Actuators……...…………………..…………………………..33

4.5.3 H-Bridge………………………...…………………..………..33

4.5.4 Microcontroller 89c51………………………....................…..34

4.6 Proteus design………………….

………………………………………....37

4.7 Motor driving circuitry………..………………………………………….38

4.8 PCB designing………………..…………………………………………..39

4.9 Problems and troubleshooting………………...…………………….........39

Chapter 5: Results and Discussion.…………………………..……………41

5.1 Graphical user interface………...………………………...……………...41

VI

5.2 GUI in Matlab…………………………………….……………………...41

5.2.1 How does GUI work……………………...……………….....42

5.2.1 How does GUI

work..............................................................................................46

5.2.2 Ways to built Matlab

GUI........................................................43

5.3 Creating GUI with guide………………………………………...…….....43

5.3.1 What is guide?...........................................................................43

5.3.2 Opening guide…………………………..………....................43

5.3.3 Laying out a guide to GUI ………………………………….. 44

5.3.4 Adding components to GUI……......…………….………......45

5.4 Our project GUI...……………………………………....….....……….....45

5.4.1 Start……………………………………..……………………46

5.4.2 Trouble shoot…………………………….…….…………….47

5.4.3 Training……………...……………………..……….……..…48

5.4.4 Simulation result…..………………….…….………………..49

Chapter 6: Conclusion.…………..…………………………………………50

Future Suggestions………………………………………………………….51

References…………………………………………………………………...52

Appendix A

Feature Extraction, Computing Statistics, Neural network codes…………....54

Appendix B

Components cost…………….……………………………………………….61

Appendix C

Data Sheets…………………………………………………………………...62

VII

LIST OF FIGURES

Figure 2.1: Project Design……………………………………………….......5

Figure 3.1: Style of neural computation…………………………………….10

Figure 3.2: Network/data manager in nntool………………………………..11

Figure 3.3: Create new data in nntool.......................................................... .12

Figure 3.4: Create network in nntool.............................................................13

Figure 3.5: Hard limit transfer function ……………………………..…..….13

Figure 3.6: Pure line transfer function ……………………………..…..…...14

Figure 3.7: Log sigmoid transfer function.................................................…14

Figure 3.8: View of the project network in nn tool…………………….........15

Figure 3.9 Feed farward back propagation....................................................16

Figure 3.10: Result of the trainning...............................................................16

Figure 4.1: Sampling rate...............................................................................23

Figure 4.2: Retrieving...................................................…..............................23

Figure 4.3: Hamming Window......................................................................25

Figure 4.4: Result of hamming Window........................................................26

Figure 4.5: Block diagram of voiced/unvoiced classification.......................28

Figure 4.6: Define of zero crossing rate.........................................................29

Figure 4.7: Distribution of zero crossing for voiced/unvoiced speech..........29

Figure 4.8: Computation of short time energy ………….…………………..31

VIII

Figure 4.9: DC motor……………………….……………………………….33

Figure 4.10: Diagram of l293D......................................................................34

Figure 4.11: Pin Description..........................................................................35

Figure 4.12: Circuit diagram of Motor driving circuit...................................37

Figure 4.13: Motor Driving Circuitry……………………………………….38

Figure 4.14: Drive layout …………………………………………………...39

Figure 5.1: Guide layout editor ……………………………………………..44

Figure 5.2: Adding component to GUI ……………………………………..45

Figure 5.3: Our project GUI…………………………..……………….…….45

Figure 5.4: Start button ……………………………………………………..46

Figure 5.5: Troubleshoot button…………………………………………….47

Figure 5.6: Trainning button ………………………………………….…….48

Figure 5.7: Simmulation button …………………………………………….49

IX

LIST OF TABLES

Table 4.1: Pin configuration …………………….……………………….36

X

CHAPTER 1:

Introduction

1.1 Problem Statement

Implementation of voice command and control for a cyborg hand requires

extraction of voice feature that make a voice command distinguishable from

all other voices in the language.

Feature extraction requires that voice should be clean from all noise so that

feature extracted per frame of a voice command should have a value within

the range of the class the voice command belongs.

Pattern recognition techniques are applied on the features so that they can be

classified. Pattern recognition techniques should be able to learn the pattern

present in the dataset of a feature extracted from a same voice command class

Combining the feature extraction and pattern recognition, the cyborg hand can

be made intelligent to act on the voice command from a single user.

1.2 Objective

Prime objective of our project is to develop a voice recognition system that

can be used as “command and control” for any machine.

For that we have chosen robotic arm. Since it has an extensive set of

commands which can be used to train our voice recognition system.

Most of the voice recognition systems are developed for speech to text

conversion. Since these systems are operated for a large vocabulary they are

less accurate and require more computation. our aim was to develop a voice

1

Air University Introduction

recognition for the command and control of a machine, while using less

computational power by using non conventional approach to voice

recognition i.e. instead of using convention features of voice(e.g. MFCC and

LPC) we have chosen a set five feature that make a voice distinguishable

enough to control a cyborg hand.

In our project we achieve the accuracy of 95%, for a single user.

Voice command and control has many applications from military to air force

and from telephony to people with disabilities.

Voice command and control is used in military ground vehicles where

commander can rotate and aim its gun on the target through his voice while

sitting inside in the safe environment of tank. This application of voice

command and control was our major source of inspiration for making this

project since a robotic arm and machine gun has the difference of only the end

effector.

1.3 Organization of Project Report

In Front end processing, we first take the voice signal from the user with the

help of microphone at the sample rate of 44100 samples per second and save it

in the hard drive. Now the samples are retrieving in the MATLAB by using

the command wavered. Normalizing, framing, and windowing are also done in

the first phase of Front end processing.

In the feature extraction, the voice samples are receive by each feature block

and then by applying statistics on each feature block we get the output in the

form of vector matrix. This vector matrix is now applied at the input of neural

network.

In the neural network, neural network is applied at vector matrix of speech

signal and trained the neural network and matched it according to target value

by adjusting weights and bias value.

In cyborg hand, the output of neural network is sent to robotic circuit via

parallel cable which commands the robotic arm to move in specific directions.

2

CHAPTER 2:

Literature Review

2.1 Aim and Purpose

To implement a speech recognition system capable of recognizing spoken

words from a small vocabulary and using those words to control the motions

of cyborg hand. Secondly to “grip” any abject by rotating its fingers. The

rotation of finger joints will stop by sensing an object to hold it. An artificial

device extension that replaces a missing body part. It is part of the field

of biomechatronics, the science of using mechanical devices with

human muscle, skeleton, and nervous systems to assist or enhance motor

control lost by trauma, disease, or defect. Prostheses are typically used to

replace parts lost by injury (traumatic) or missing from birth or to supplement

defective body parts. Inside the body, artificial heart valves are in common use

with artificial hands seeing less common use but under active technology

development. Other medical devices and aids that can be considered

prosthetics include hearing aids, artificial eyes, palatal obturator, gastric

bands, and dentures. Although among designs of prosthetics there have been

some successes in the design of functional robotic implants, there remain

many issues and challenges concerned with the failure to meet the ‘ideal’

requirements of a satisfactory prosthetic. These ‘ideals’ require the device to

be easy to control, comfortable to wear, and cosmetically pleasing.

3

Air University Literature Review

2.2 Mechanical Assembly Review

The literature on prosthetics and robotic implants are voluminous, this review

focuses on four topics to determine key challenges and opportunities

underlying these interdisciplinary research areas: firstly, an artificial hand as a

biomimetic; secondly, prosthetic implants thirdly, prosthetic implants and

tissue reactions to the material fourthly, how inflammatory responses of cells

and tissues surrounding implanted sensors interfere with the signal

transmission of such sensors. This review also notes the importance of the

biological interfaces that robotic implants and other prosthetic devices are in

contact with and how an improved knowledge of pathophysiological changes

at such biological interfaces will lead to improved and more biocompatible

designs of prosthetics. This review concludes with the vision that, to develop a

design that satisfies the above ‘ideals’, an interdisciplinary team of biomedical

and tissue engineers, and biomaterial and biomedical scientists is needed to

work together holistically and synergistically.

This report presents an overview of speech recognition technology, software,

development and applications. It begins with a description of how such

systems work, and the level of accuracy that can be expected. Applications of

speech recognition technology in education and beyond are then explored. A

brief comparison of the most common systems is presented, as well as notes

on the main centres of speech recognition research in the UK educational

sector. The report concludes with potential uses of speech recognition in

education, probable main uses of the technology in the future. Speech

recognition is an alternative to traditional methods of interacting with a

computer, such as textual input through a keyboard. An effective system can

replace, or reduce the reliability on, standard keyboard and mouse input. This

can especially assist the following:

· People who have little keyboard skills or experience, who are slow typists,

or do not have the time or resources to develop keyboard skills.

4

Air University Cyborg Hand

· Dyslexic people or others who have problems with character or word use and

manipulation in a textual form.

· People with physical disabilities that affect either their data entry, or ability

to read (and therefore check) what they have entered.

2.3 Speech Recognition Review

Speech recognition consists of following:

A microphone, for the person to speak into.

Speech recognition software.

A computer to take and interpret the speech.

A good quality sound card for input and output.

The basic block diagram of our project is:

Fig 2.1: Project Design

5

CHAPTER 3:

Design Procedure

3.1 Artificial intelligence

It is the property of machines to do reasoning for any given problem with

specified logics and perceptions after a particular process known as “Machine

learning”.

3.2 Machine Learning

Machine learning, a branch of artificial intelligence, is a scientific discipline

that is concerned with the design and development of algorithms that allow

computers to evolve behaviors based on empirical data, such as from sensor

data or databases. A learner can take advantage of examples (data) to capture

characteristics of interest of their unknown underlying probability distribution.

Data can be seen as examples that illustrate relations between observed

variables. A major focus of machine learning research is to automatically learn

to recognize complex patterns and make intelligent decisions based on data;

the difficulty lies in the fact that the set of all possible behaviors given all

possible inputs is too large to be covered by the set of observed examples

(training data). Hence the learner must generalize from the given examples, so

as to be able to produce a useful output in new cases. Machine learning, like

all subjects in artificial intelligence, require cross-disciplinary proficiency in

several areas, such as probability theory, statistics, pattern recognition,

cognitive science, data mining, adaptive control, computational neuroscience

and theoretical computer science.

6


3.2.1 Definition

A computer program is said to learn from experience E with respect to some

class of tasks T and performance measure P, if its performance at tasks in T, as

measured by P, improves with experience E.

3.2.2 Generalization

The core objective of a learner is to generalize from its experience. The

training examples from its experience come from some generally unknown

probability distribution and the learner has to extract from them something

more general, something about that distribution, which allows it to produce

useful answers in new cases.

3.3 Human interaction

Some machine learning systems attempt to eliminate the need for human

intuition in data analysis, while others adopt a collaborative approach between

human and machine. Human intuition cannot, however, be entirely eliminated,

since the system's designer must specify how the data is to be represented and

what mechanisms will be used to search for a characterization of the data.

3.4 Algorithm types

Machine learning algorithms are organized into taxonomy, based on the

desired outcome of the algorithm.

Supervised learning generates a function that maps inputs to desired

outputs. For example, in a classification problem, the learner

approximates a function mapping a vector into classes by looking at

input-output examples of the function.

Unsupervised learning models a set of inputs, like clustering.

Semi-supervised learning combines both labeled and unlabeled

examples to generate an appropriate function or classifier.

7

Air University Design Procedure

Reinforcement learning learns how to act given an observation of the

world. Every action has some impact in the environment, and the

environment provides feedback in the form of rewards that guides the

learning algorithm.

Transduction tries to predict new outputs based on training inputs,

training outputs, and test inputs.

Learning to learn learns its own inductive bias based on previous

experience.

3.4.1 Supervised learning

Artificial neural network

o Back propagation

Bayesian statistics

o Naive Bayes classifier

o Bayesian network

o Bayesian knowledge base

Case-based reasoning

Decision trees

Inductive logic programming

Gaussian process regression

Group method of data handling (GMDH)

Learning Automata

Minimum message length (decision trees, decision graphs, etc.)

Lazy learning

Instance-based learning

o Nearest Neighbor Algorithm

Probably approximately correct learning (PAC) learning

Ripple down rules, a knowledge acquisition methodology

Symbolic machine learning algorithms

Sub symbolic machine learning algorithms

8


Support vector machines

Random Forests

Ensembles of classifiers

o Bootstrap aggregating (bagging)

o Boosting

Ordinal classification

Regression analysis

Information fuzzy networks (IFN)

3.4.2 Statistical classification

Linear classifiers

o Fisher's linear discriminator

o Logistic regression

o Naive Bayes classifier

o Perceptron

o Support vector machines

Quadratic classifiers

k-nearest neighbor

Boosting

Decision trees

o C4.5

o Random forests

Bayesian networks

Hidden Markov models

3.5 Artificial neural network

An Artificial Neural Network (ANN) is an information processing paradigm that

is inspired by the way biological nervous systems, such as the brain, process

information. The key element of this paradigm is the novel structure of the

information processing system. It is composed of a large number of highly

9


interconnected processing elements (neurons) working in unison to solve

specific problems. ANN’s, like people, learn by example. An ANN is

configured for a specific application, such as pattern recognition or data

classification, through a learning process [6].

3.5.1 ANN in Matlab

Artificial neural networks (ANN) are among the newest signal-processing

technologies in the engineer's toolbox. In engineering, neural networks serve

two important functions: as pattern classifiers and as nonlinear adaptive filters.

Definitions and Style of Computation An Artificial Neural Network is an

adaptive, most often nonlinear system that learns to perform a function (an

input/output map) from data.

Fig 3.1: Style of neural computation…………..[8]

Adaptive means that the system parameters are changed during operation,

normally called the training phase. After the training phase the Artificial

Neural Network parameters are fixed and the system is deployed to solve the

problem at hand (the testing phase). The Artificial Neural Network is built

10


with a systematic step-by-step procedure to optimize a performance criterion

or to follow some implicit internal constraint, which is commonly referred to

as the learning rule [9]. The input/output training data are fundamental in

neural network technology, because they convey the necessary information to

"discover" the optimal operating point.. There is a style in neural computation

that is worth describing.

Fig 3.2: Network/data manager in nntool

An input is presented to the neural network and a corresponding desired or

target response set at the output. An error is composed from the difference

between the desired response and the system output. This error information is

fed back to the system and adjusts the system parameters in a systematic

fashion (the learning rule). The process is repeated until the performance is

acceptable [8]. It is clear from this description that the performance hinges

heavily on the data. In artificial neural networks, the designer chooses the

network topology, the performance function, the learning rule, and the

criterion to stop the training phase, but the system automatically adjusts the

parameters. At present, artificial neural networks are emerging as the

technology of choice for many applications, such as pattern recognition,

prediction, system identification, and control.

3.5.2 How Does an ANN Work?

In the ANN described above the user selects a tool by writing in m file

“nntool”.

User selects data and target value from “new” or if we have data in any file

than data can be imported by “Import”. Target is value from which we match

our output value

11


Fig 3.3: Create new data in nntool

Than user create network in which training function, transfer function,

number of layer and number of neuron in each layer is selected.

Fig 3.4:Create network in nntool

3.6 Transfer Functions

Three of the most commonly used functions are shown below:

12


3.6.1 Hard-limit transfer function

Fig 3.5: Hard limit transfer function………….[9]

The hard-limit transfer function shown above limits the output of the neuron

to either 0, if the net input argument n is less than 0; or 1, if n is greater than

or equal to 0.

3.6.2 Linear transfer function

Fig 3.6: Pure line transfer function……………….[9]

The linear transfer function gives the value of positive value for input value

greater than one and negative one for input value less than one.

3.6.3 Sigmoid transfer function

13


The sigmoid transfer function shown below takes the input, which may have

any value between plus and minus infinity, and squashes the output into the

range 0 to 1.

Fig 3.7: Log sigmoid transfer function…………..[9]

In our project we use three layers of neuron , in first layer we select five

neuron for five features of voice and in second layer we select twenty neuron

in hidden layer and in third layer one neuron is selected for output [11].

In our project we used the feed-forward back propagation. The view of our

network is given below

.

Fig 3.8: View of the project network in nn tool

3.6.4 Feed-forward back propagation

A feed forward neural network is an artificial neural network where

connections between the units do not form a directed cycle. This is different

from recurrent neural networks.

In this network, the information moves in only one direction, forward, from

the input nodes, through the hidden nodes (if any) and to the output nodes.

There are no cycles or loops in the network [10].

14


In Back-propagation output values are compared with the correct answer to

compute the value of some predefined error-function. By various techniques,

the error is then fed back through the network. Using this information, the

algorithm adjusts the weights of each connection in order to reduce the value

of the error function by some small amount. After repeating this process for a

sufficiently large number of training cycles, the network will usually converge

to some state where the error of the calculations is small. In this case, one

would say that the network has learned a certain target function.

Fig 3.9: Feed farward back propagation……………..[6]

The result of training is given below.

15


Fig 3.10: Result of the trainning

3.6.5 Epoch

An entire pass through all of the input training vectors is called an epoch.

When such an entire pass of the training set has occurred without error,

training is complete [11].

One iteration through the process of providing the network with an input and

updating the network's weights. Typically many epochs are required to train

the neural network.

16


3.6.6 Training of ANN

If sim and learnp are used repeatedly to present inputs to a perceptron, and to

change the perceptron weights and biases according to the error, the

perceptron will eventually find weight and bias values that solve the problem,

given that the perceptron can solve it. Each traverse through all of the training

input and target vectors is called a pass [12].

The function train carries out such a loop of calculation. In each pass the

function train proceeds through the specified sequence of inputs, calculating

the output, error and network adjustment for each input vector in the sequence

as the inputs are presented.

3.7 Mechanical Assembly

A robotic arm is a robotic manipulator, usually programmable, with similar

functions to a human arm. The links of such a manipulator are connected by

joints allowing either rotational motion (such as in an articulated robot) or

translational (linear) displacement. The links of the manipulator can be

considered to form a kinematics chain. The business end of the kinematics

chain of the manipulator is called the end effector and it is analogous to the

human hand. The end effector can be designed to perform any desired task

such as welding, gripping, spinning etc., depending on the application [14].

3.7.1 Types of Industrial Robots

Cartesian robot /Gantry robot: Used for pick and place work,

application of sealant, assembly operations, handling machine tools and

arc welding. It's a robot whose arm has three prismatic joints, whose axes

are coincident with a Cartesian coordinator.

Cylindrical robot: Used for assembly operations, handling at machine

tools, spot welding, and handling at die-casting machines. It's a robot

whose axes form a cylindrical coordinate system.

17


Spherical/Polar robot: Used for handling at machine tools, spot welding,

die-casting, fettling machines, gas welding and arc welding. It's a robot

whose axes form a polar coordinate system.

SCARA robot: Used for pick and place work, application of sealant,

assembly operations and handling machine tools. It's a robot which has

two parallel rotary joints to provide compliance in a plane [15].

Articulated robot: Used for assembly operations, die-casting, fettling

machines, gas welding, arc welding and spray painting. It's a robot whose

arm has at least three rotary joints.

Parallel robot: One of the use is mobile platform handling cockpit flight

simulators. It's a robot whose arms have concurrent prismatic or rotary

joints.

Our voice controlled cyborg arm is a “fixed sequence cylindrical robot”. It is a

robot whose motion along an axis is limited and the motion between these axis

can not be stopped. This robot performs operation according to preset

information that cannot be easily changed [14].

3.7.2 Components of Cyborg Hand

Power Source

We use a step down transformer that converts 220 AC into 12V DC.

Actuator

Actuators are like the "muscles" of a robot, the parts which convert stored

energy into movement. In our project actuators are 3 DC motors that spin

gears.

Platform

Our robotic arm is supported by an iron stand that is fixed on the surface

of a steel base. Robotic arm is made of light weighted assembly.

18


End Effectors

In robotics, an end effector is the device at the end of a robotic arm,

designed to interact with the work environment.

Metallic Iron String

Iron string is connected from the lever to the upper end of the finger to

rotate the finger with motor.

Spring

Spring is conncted on the back side of finger from its upper to the suppor-

ting iron sheet..

3.8 Mechanical Construction

We have design five fingered hand with 5 active fingers. Each finger has 2

joints. Finger joints are controlled by thick iron wires. Iron wires are

connected to the servo motors via lever. Servo motor is controlled by the

controlling circuitry. Controlling circuitry is interfaced with microcontroller.

We have designed another axis, the wrist joint. Wrist can move clockwise and

anti-clockwise. The fifth axis is at elbow. Elbow is manually movable. The

thumb, first and fifth finger is active in order to grip an object easily. Metallic

(Steel) sheet is used to make fingers and thumb. The arm is also consisting of

metal. The leather glove will cover its fingers. The weight is about 3kg to 4kg.

In our project an end effector is a mechanical gripper. It consists of just two

fingers, that can opened and closed to pick up and let go of a range of small

objects. We use a thread and two small springs. When motor rotates in one

direction, thread is made to wrapped on its shaft, as a result springs become

stretched and gripper fingers open. When motor rotates in other direction

thread becomes unwrapped. As a result springs compress and gripper closed.

3.9 Characteristics of Cyborg Arm

(1) Joints

19


Our robotic arm has two joints called rotary joint and linear joint.

Rotary joint: It can rotate the robotic arm’s end effector 270 degrees. This

joint connects robotic arm with the iron stand through a gear.

Linear joint: It can rotate the robotic arm’s end effector along the radius of

270 degrees. This joint connects the end effector with the robotic arm

through a gear [15].

(2) Payload

The carrying capacity of a robotic arm is called its payload. Our robotic

arm can pick an object up to 300 grams.

(3) Reach

The end effector (gripper) of our robotic arm can move up to 12 inches on

a lead screw.

(4) Repeatability

A measurement may be said to be repeatable when this variation is small-

ar than some agreed limit. The repeatability of this robotic arm is ±1.5.

(5) Axis of Rotation

Our robotic arm can move in cylindrical co-ordinates having a constant z

parameter. It can rotate 270 degrees in circular motion and its end effectors

can move 12 inches linearly.

3.10 Finger Construction with spring

20


A finger having two axes of rotation. These two axes are at each finger at its

both two joints. These axes are at the corner of a finger in order to provide

better rotation. Metallic (iron) string is connected from lever to the upper end.

When motor rotates clockwise, the finger rotates anti-clockwise. Its rotation is

around its both 1st and 2nd axes (at joints) in two steps. When motor rotates

anti-clockwise, the finger rotates clockwise. Spring is connected at back

portion of finger. When motor is rotated anti-clockwise, the then the lever

looses its grip on finger. The spring stretches the finger back by moving both

joints around their axes in two steps. With the attachment of spring, the

complexity of circuit reduces. Use of spring makes circuit economic and

efficient.

3.10.1 How to Grip

When both joint of all fingers move anti-clockwise, the hand turn to close. Put

any object in between its rotating fingers. When finger tips touches the surface

of that object, the current in the circuit sustains. If we do not put a limiting

circuit, the current in the circuit will not stop, causes a chance to break the

joints.

21

CHAPTER 4:

Design Details

4.1 Introduction

Front end are generalized terms that refer to the initial and the end stages of a

process. The front end is responsible for collecting input in various forms from

the user and processing it to conform to a specification the back end can use.

The front end is an interface between the user and the back end. In our project

front end processing has two phases. In first phase we have done sampling and

recording, retrieving and normalizing, and finally framing and windowing. The

second phase involves feature extraction [4].

4.2 First phase

First phase is sampling and recording.

4.2.1 Sampling rate

The sampling rate, sample rate, or sampling frequency defines the number

of samples per second (or per other unit) taken from a continuous signal to make

a discrete signal. For time-domain signals, the unit for sampling rate

is hertz (inverse seconds, 1/s, s−1). The inverse of the sampling frequency is

the sampling period or sampling interval, which is the time between samples.

Sample rate is usually noted in Sa/s (non-SI) and expanded as kSa/s, MSa/s, etc.

The common notation for sampling frequency is fs.

22


Fig 4.1: Sampling rate……………………..[4]

.The sampling rate in our project is 44100 samples per sec

4.2.2 Recording

We take our voice with the help of microphone and record or saved it in the hard

drive with the sampling rate (44100 samples per sec).

4.2.3 Retrieving

Retrieving is done at the sample rate of 44100 samples per sec through wave

read function of MAT.LAB.

Fig 4.2: Retrieving

23

Air University Design Details

After retrieving the copies of voice command signals is sent to each feature

extraction blocks.

4.2.4 Normalizing

The process of normalizing is called pre emphasis.

In this process all the samples in the voice signal are divided by the maximum

value of a sample. It is done to reduce the dynamic range of the signal and to

make it spectrally flat.

4.2.5 Framing

Framing is called frame blocking.

In this step the voice command signal is divided into a no of blocks. Each frame

(block) contain equal no of voice samples.

4.2.6 Windowing

Windowing is done to remove the discontinuities at the start and at the end of

the frame. We have employed Hamming window for this purpose.

Hamming window

It is also known as a raised cosine window. The hamming window for N points

is defined as:

W (i) =0.54+0.46*cos(2i/N)

Where –N/2<=I<N/2

24


Fig 4.3: Hamming Window

These are specific examples from a general family of curves of the form

W (i) = a + (1 - a) cos (2 pi i / N)

25


Fig 4.4: Result of hamming Window

26


4.3 Feature Extraction

The second phase of front end processing in our project is feature extraction.

After the first phase the voice samples are sent to each feature extraction

block. The voice features are extracted per frame of voice signal. The five

features of two types are extracted per frame. The two types are time domain

and spectral feature. The five features we have used in our project are short

time energy, zero crossing rate, spectral roll off, spectral flux, and spectral

centroid. The first two features are time domain and last three are spectral

features. Statistics are involved to each feature block to generalize a feature’s

value over a entire signal. The statistics are involved to each feature extraction

block because the pattern recognition technique requires that the feature

should give a single value over e entire signal. Due to which statistics are

involved to represent a general trend in voice command signal. As we used

five features, every feature should give a single value so vector of five values

is given to the input of the neural network. For example, zcr is first calculated

per frame after that a mean value of it is calculated using all of its value per

frame. This value is now ready for input of pattern recognition. Similarly the

statistics of other features are calculated as follows:

4.4 Features

We have extracted the following features:

27


4.4.1 Zero-Crossings Rate

Zero-crossing rate is an important parameter for voiced/unvoiced

classification. It is also often used as a part of the front-end processing in

automatic speech recognition system. The zero crossing count is an indicator

of the frequency at which the energy is concentrated in the signal spectrum

[5].

The analysis for classifying the voiced/unvoiced parts of speech has been

illustrated in the block diagram

in Fig.4.5…………………[5]

Fig 4.5: Block diagram of voiced/unvoiced classification

In the context of discrete-time signals, a zero crossing is said to occur if

successive samples have different algebraic signs. The rate at which zero

28


crossings occur is a simple measure of the frequency content of a signal. Zero-

crossing rate is a measure of number of times in a given time interval/frame

that the amplitude of the speech signals passes through a value of zero. Speech

signals are broadband signals and interpretation of average zero-crossing rate

is therefore much less precise.

However, rough estimates of spectral properties can be obtained using a

representation based on the short- time average zero-crossing rate.

Fig 4.6: Define of zero crossing rate..[5] Fig 4.7: Distribution of zero

crossing for voiced/unvoiced speech

29


4.4.2 Short time energy

The short time energy measurement of a speech signal can be used to

determine voiced vs. unvoiced speech. Short time energy can also be used to

detect the transition from unvoiced to voiced speech and vice versa [5]. The

energy of voiced speech is much greater than the energy of unvoiced speech. .

Short-time energy can define as:

The choice of the window determines the nature of the short-time energy

representation. In our model, we used Hamming window. The hamming

window gives much greater attenuation outside the band pass than the

comparable rectangular window.

h(n) = 0.54 − 0.46 cos(2πn /( N − 1)) , 0 ≤ n ≤ N − 1

30


h(n) = 0 , otherwise

Fig 4.8: Computation of short time energy……….. [5]

The attenuation of this window is independent of the window duration.

Increasing the length, N, decreases the bandwidth, Fig 5. If N is too small,

details of the waveform. If N is too large, E n will change very slowly and thus

will not adequately reflect the changing properties of the speech signal.

4.4.3 Spectral Roll off

Description:

The spectral Roll Offs point R determines where 85% of the window’s

energy is achieved. It is used to distinguish voiced from unvoiced

speech and music.

Resolution: window

Parameters: SOUND file, start time, end time based on the Sub bands

RMS.

Formula:

31


4.4.4 Spectral Flux

Description:

It determines changes of spectral energy distribution of two successive

windows

Resolution: window.

Parameters: SOUND file, start time, end time.

Based on the Sub band RMS.

4.4.5 Spectral Centroid

Description:

The spectral centroid is the balancing point of the sub band energy

distribution. It determines the frequency area around which most of the

signal energy concentrates and is thus closely related to the time-

domain Zero Crossing Rate feature. It is also frequently used as an

approximation for a perceptual brightness measure.

Resolution: window.

Parameters: SOUND file, start time, end time, start sub band number,

end sub band number.

Based on the Sub band RMS.

4.5 Electrical/Electronic Components

Electrical and electronics components are D.C supply, voltage regulator

(7805), capacitors, resistors, NPN transistors (PN2222), , H-bridge(L298) and

diodes (1N4001),25 db parallel cable and 25 db female connector.

32


4.5.1 D.C Power Supply

DC Power Supply of 12 volts and -12 volts is used in the project. DC Power

Supply consists of a transformer, voltage regulator and four diodes or a bridge.

4.5.2 Actuators

Solenoid actuated valves and a D.C Motor are used as actuators. Output

voltage of the first relay is 12 volt and 5volts applied to its coil. Relays which

are connected in relay board have output voltage of 220 volts and they are

energized by first group of relays having output voltage of 12volts and .The

output of second group of relays is used to actuate the Solenoid actuated

valves.

Fig 4.9: Dc motor……………….[16]

4.5.3 H-bridge (L293D) Dual full-bridge driver

Operating supply voltage is 12V.

Total dc current up to 800mA.

Low saturation voltage

33


Over temperature protection

Logical”0” input voltage up to 1.5 v

( High noise immunity)

Fig 4.10: Diagram of l293D……………………[17]

4.5.4 Microcontroller 89C51

A microcontroller (also MCU or µC) is a computer-on-a-chip, containing a

processor, memory, and input/output functions. It is a microprocessor

emphasizing high integration.

34


Fig 4.11: Pin Description

35


Table 4.1: Pin Description

Pin No Function Name1

8 bit input/output port (P1) pins

P1.02 P1.13 P1.24 P1.35 P1.46 P1.57 P1.68 P1.79 Reset pin; Active high Reset

10Input (receiver) for serial communication

RxD

8 bit input/output port (P3) pins

P3.0

11Output (transmitter) for serial communication

TxD P3.1

12 External interrupt 1 Int0 P3.213 External interrupt 2 Int1 P3.314 Timer1 external input T0 P3.415 Timer2 external input T1 P3.516 Write to external data memory Write P3.6

17Read from external data memory

Read P3.7

18Quartz crystal oscillator (up to 24 MHz)

Crystal 219 Crystal 120 Ground (0V) Ground21

8 bit input/output port (P2) pins/High-order address bits when interfacing with external memory

P2.0/ A8

22 P2.1/ A9

23 P2.2/ A10

24 P2.3/ A11

25 P2.4/ A12

26 P2.5/ A13

27 P2.6/ A14

28 P2.7/ A15

29Program store enable; Read from external program memory

PSEN

30Address Latch Enable ALEProgram pulse input during Flash programming Prog

31

External Access Enable; Vcc for internal program executions

EA

Programming enable voltage; 12V (during Flash programming)

Vpp

32

8 bit input/output port (P0) pins Low-order address bits when interfacing with external memory

P0.7/ AD7

33 P0.6/ AD6

34 P0.5/ AD5

35 P0.4/ AD4

36 P0.3/ AD3

37 P0.2/ AD2

38 P0.1/ AD1

39 P0.0/ AD0

40 Supply voltage; 5V (up to 6.6V) Vcc

36


4.6 Proteus Design:

The proteus design of our circuit of motor interfacing with microcontroller is

as follows:

Fig 4.12: circuit diagram of Motor driving circuit

37


4.7 Motor Driving Circuitry

The DC motor drive is used. The drive can rotate the motor in clockwise

direction in order to close the fingers by moving joints. The three motors can

move simultaneously in order to grip an object.

Fig 4.13: Motor Driving Circuitry

We have used the PNP and NPN transistors D313 and B1367. The rating of

motor is about 12V/ 1 A. Both transistors are used as switch in saturation

mode. By switching, we have given the 0V to PNP then motor rotates in

clockwise direction. When 0 is given to NPN transistor, then motor rotates in

anti-clockwise direction. We have used two free wheeling diodes in parellal

with both transistors in reverse bias. When current passes through motor, then

inductors of motor gets charged. These inductors produce back EMF which

can damage the circuit. So these diodes work as protection circuit. Then we

have connected the two switches with the two back to back diodes in parallel.

These switches work as limiting switches. When the lever touches the switch,

then one switch cut off the circuit in one clockwise extreme position and when

38


the motor rotates anti- clockwise direction then the lever touches the other

switch to cut off the circuit at other extreme position. Four resistances of

1kohm are used to limit the current and to prevent the short circuiting.

4.8 PCB designing

A printed circuit board, or PCB, is used to mechanically support the electrical

and electronics components more precisely then other boards like Vero board

and brad boards. So it is more secure and effective then others. Considering

all the above facts we decided to use PCB for the hardware implementation of

our project for the motor drivers and the microcontroller boards as well.

The layout of our motor drive is as follows:

Fig 4.14: Drive layout

4.9 Problems and Troubleshooting

Problems

Selection of optimal software.

39


Selection of mathematical techniques and codes regarding software’s

toolbox.

Designing lay-out of physical model.

Design of appropriate electronic hardware.

Unavailability of components while designing hardware.

Selection an optimal designing tool for hardware component’s

modeling and simulation.

Troubleshooting

We worked and discussed on various software’s while surfing on the

net and interfacing with our teachers but ended up on deciding to work

on Matlab.

Among several techniques, to convert voice to do the desired action we

selected the features of voice and apply neural network.

We consider several factors like weight and then choose suitable

material for fabrication of our physical model.

Amongst several designing software’s we adopted PROTEUS for

designing of electronic components integration.

40


41

CHAPTER 5:

Results and Discussion

5.1 Graphical User Interface

In computing a graphical user interface (GUI, sometimes pronounced gooey)

is a type of user interface that allows users to interact with electronic devices

with images rather than text commands. GUIs can be used in computers, hand-

held devices such as MP3 players, portable media players or gaming devices,

household appliances and office equipment. A GUI represents the information

and actions available to a user through graphical icons and visual indicators

such as secondary notation, as opposed to text-based interfaces, typed

command labels or text navigation. The actions are usually performed through

direct manipulation of the graphical elements [13].

5.2 GUI in Matlab

A graphical user interface (GUI) is a graphical display in one or more

windows containing controls, called components that enable a user to perform

interactive tasks. The user of the GUI does not have to create a script or type

commands at the command line to accomplish the tasks. Unlike coding

programs to accomplish tasks, the user of a GUI need not understand the

details of how the tasks are performed.

GUI components can include menus, toolbars, push buttons, radio buttons, list

boxes, and sliders—just to name a few. GUIs created using MATLAB tools

can also perform any type of computation, read and write data files,

communicate with other GUIs, and display data as tables or as plots.

42

http://en.wikipedia.org/wiki/Computing

http://en.wikipedia.org/wiki/Direct_manipulation

http://en.wikipedia.org/wiki/Text-based_(computing)

http://en.wikipedia.org/wiki/Secondary_notation

http://en.wikipedia.org/wiki/Icon_(computing)

http://en.wikipedia.org/wiki/MP3

http://en.wikipedia.org/wiki/Mobile_device

http://en.wikipedia.org/wiki/Mobile_device

http://en.wikipedia.org/wiki/Computer

http://en.wikipedia.org/wiki/Human-computer_interaction

http://en.wikipedia.org/wiki/User_(computing)

http://en.wikipedia.org/wiki/User_interface


5.2.1 How Does a GUI Work?

In the GUI described above, the user selects a data set from the pop-up menu,

and then clicks one of the plot type buttons. The mouse click invokes a

function that plots the selected data in the axes.

Most GUIs wait for their user to manipulate a control, and then respond to

each action in turn. Each control, and the GUI itself, has one or more user-

written routines (executable MATLAB code) known as callbacks, named for

the fact that they "call back" to MATLAB to ask it to do things. The execution

of each callback is triggered by a particular user action such as pressing a

screen button, clicking a mouse button, selecting a menu item, typing a string

or a numeric value, or passing the cursor over a component. The GUI then

responds to these events. You, as the creator of the GUI, provide callbacks

which define what the components do to handle events.

This kind of programming is often referred to as event-driven programming.

In the example, a button click is one such event. In event-driven programming,

callback execution is asynchronous, that is, it is triggered by events external to

the software. In the case of MATLAB GUIs, most events are user interactions

with the GUI, but the GUI can respond to other kinds of events as well, for

example, the creation of a file or connecting a device to the computer.

You can code callbacks in two distinct ways:

As MATLAB language functions stored in files

As strings containing MATLAB expressions or commands (such as 'c

= sqrt(a*a + b*b);'or 'print')

Using functions stored in code files as callbacks is preferable to using strings,

as functions have access to arguments and are more powerful and flexible.

MATLAB scripts (sequences of statements stored in code files that do not

define functions) cannot be used as callbacks.

Although you can provide a callback with certain data and make it do

anything you want, you cannot control when callbacks will execute. That is,

43

Air University Results and Discussion

when your GUI is being used, you have no control over the sequence of events

that trigger particular callbacks or what other callbacks might still be running

at those times. This distinguishes event-driven programming from other types

of control flow, for example, processing sequential data files.

5.2.2 Ways to Build MATLAB GUIs

A MATLAB GUI is a figure window to which you add user-operated

controls. You can select, size, and position these components as you like.

Using callbacks you can make the components do what you want when the

user clicks or manipulates them with keystrokes.

You can build MATLAB GUIs in two ways:

Use GUIDE (GUI Development Environment), an interactive GUI

construction kit.

Create code files that generate GUIs as functions or scripts

(programmatic GUI construction).

5.3 Creating GUIs with GUIDE

5.3.1 What is GUIDE?

GUIDE, the MATLAB Graphical User Interface Development Environment,

provides a set of tools for creating graphical user interfaces (GUIs). These

tools greatly simplify the process of lying out and programming GUIs.

5.3.2 Opening GUIDE

There are several ways to open GUIDE from the MATLAB Command line.

You can also right-click a FIG-file in the Current Folder Browser and select

Open in GUIDE from the context menu.

44


When you right-click a FIG-file in this way, the figure opens in the GUIDE

Layout Editor, where you can work on it.

.

Fig 5.1: Guide layout editor

All the tools in palette have tool tips. Setting a GUIDE preference lets you

display the palette in GUIDE with tool names or just their icons. See GUIDE

Preferences for more information

5.3.3 Laying Out a GUIDE GUI

The GUIDE Layout Editor enables you to populate a GUI by clicking and

dragging GUI components into the layout area. There you can resize, group

and align buttons, text fields, sliders, axes, and other components you add.

Other tools accessible from the Layout Editor enable you to:

Create menus and context menus

Create toolbars

Modify the appearance of components

Set tab order

View a hierarchical list of the component objects

Set GUI options

45

http://www.mathworks.com/help/techdoc/creating_guis/f9-998349.html

http://www.mathworks.com/help/techdoc/creating_guis/f9-998349.html


5.3.4 Adding Components to the GUI

The component palette at the left side of the Layout Editor contains the

components that you can add to your GUI. You can display it with or without

names.

Fig 5.2: Adding component to GUI……………….[13]

5.4 Our project GUI

Fig 5.3: Our project GUI

46


This is the GUI of our project. It consists of following push buttons:

1. Start

2. Troubleshoot

3. Help

4. Training

5. Simulation mode

5.4.1 Start

Fig 5.4: Start button

By pressing start button dialogue box is open which contains speak push

button, which allow user to speak with in one second time.

47


5.4.2 Troubleshoot

Fig 5.5: Troubleshoot button

By pressing trouble shoot button the dialogue box open which consist of

following buttons:

1. Rotary motor

2. Gripper open

3. Gripper close

4. Back to main menu

48


5.4.3 Training

Fig 5.6: Trainning button

By pressing training button the dialogue box open which consist of

following buttons:

1. Enter samples

2. Calculate feature vectors

3. Train ANN


49


5.4.4 Simulation Result

Fig 5.7: Simmulation button

By pressing simulation button the dialogue box open which consist of

following buttons:


2. Simulate

50

Chapter 6:

Conclusion

This report explains yhe implementation of voice controlled Cyborg Hand.The

three phases of voice recognition,along with some basis of machine learning

which were concern with the project were described.GUI designed for our

project was also explained. Robotic Hand designed and developed for the

project was also explained in detail in the report.

Voice recognition consists of three phases front-end processing, feature

extraction and pattern recognition.

Front end processing consists of sampling, recording, retrieving, framing and

windowing. The second phase of voice recognition is feature extraction, which

consists of extraction of features. The third phase is pattern recognition, which

is a major concern of machine learning. The approach of pattern recognition

implemented in our project is Artificial Neural Network. ANN used in our

project is feed-forward back propagation.

After voice recognition, the report explains the design and development of

Cyborg Arm. The robotic arm designed for our project is a fixed sequence

robotic arm with the end effecter of gripper.

To establish the coordination between robotic arm and computer, and between

human and computer a GUI was designed in our project.

Our implementation of voice recognition is 80% accurate when tested 100

times in some environment. Accuracy degrades with change in environment

due to noise. Accuracy also degrades with change in microphone and training

samples.

51


Future Suggestions

We have implemented the voice-recognition on PC. It can also be

implemented in another convinient microcontroller. The most suitable

microcontroller for that purpose is DS PIC33, since it has builtin ADC and

also have three pin port for voice recocording and playing.

Our voice recognition system some what depends upon environment. It is

because of noise (voices other than voice commands).Though we have done

the noise removal in feature extraction, in which features like ZCR detectsthe

major voice activity region in agiven sample.But the system can be made less

dependent on noise by implementing of noise removal algorithms at the front

end processing.

Moreover it has enough processing power for front end processing and pattern

recognition.

52

References

[1] Pattern Recognition. Bishop, C “Neural Networks for Pattern

Recognition”3rd Edition,1996

[2] Prasad D Polur’, Ruobing Zhou’, Jun Yang’, Fedra Adnani’, Rosalyn S.

Hobsod “SPEECH RECOGNITION USING ARTIFICIAL NEURAL”. 2001

Proceedings of the 23rd Annual EMBS International Conference, October 25-

28, Istanbul, Turkey..

[3] Roziati Zainuddin, Othman O. Khalifa.” .Neural Networks Used for

Speech Recognition”. NINETEENTH NATIONAL RADIO SCIENCE

CONFERENCE, ALEXANDRIA, March, 119-21, 2002.

[4] Lawrence Rabinar, Biing-Hwang Jaung “Fundamentals of speech

recognition”.

[5] Harmonic Spectral Centroid. McAdams, S. 1999. Perspectives on the

contribution of timbre to musical structure. Computer Music Journal.

23(3):85-102

[6] Neural Network: Eric Davalo and Patrick Naim “Neural Networks “3rd

Edition,1989

[7] Feed Forward Neural Network. http://en.wikipedia.org/wiki/ /Feed forward

neural network.

[8] Neural Network :Aleksander, I. and Morton “An introduction to neural

computing.” 2nd edition, 1992.

[9]Neural.network.http://www.emsl.pnl.gov:2080/docs/cie/neural/

neural.homepage.html

[10] Pattern Recognition. Bishop, C “Neural Networks for Pattern

Recognition”3rd Edition,1996. [11] Neural network: Howard Demuth, Mark

Beale “Neural Network Toolbox”4th Edition, July 2002

53


[12] Neural Network: Hagan,M.T. and H.B. Demuth, “Neural Networks for

Control,”Proceedings of the 1999 American Control Conference, San Diego,

CA, 1999, pp1642-1656.

[13] GUI: http:// en.wikipedia.org/wiki/Graphical_user_interface

[14] Saeed b Nikku. “Introduction to robotics”, 2nd Edition, 2001

[15] Robotic Arm .http://en.wikipedia.org/wiki/Robotic_arm.

[16] B.L.Theraja.”Electrical Machines” 8thEdition, 4 Volumes Set by Tony

Burns, Stephen...

[17] H Bridge http://en.wikipedia.org/wiki/H_bridge

[18] DB25 connector. http://www.nullmodem.com/DB-25.htm

54

Appendix A

MATLAB CODE FOR FEATURE EXTRACTION

CODE OF SHORT TIME ENERGY

Function E = ShortTimeEnergy(signal, windowLength,step);

signal = signal / max(max(signal));

curPos = 1;

L = length(signal);

numOfFrames = floor((L-windowLength)/step) + 1;

%H = hamming(windowLength);

E = zeros(numOfFrames,1);

for (i=1:numOfFrames)

window = (signal(curPos:curPos+windowLength-1));

E(i) = (1/(windowLength)) * sum(abs(window.^2));

curPos = curPos + step;

end

CODE OF SPECTRAL CENTRIOD

function C = SpectralCentroid(signal,windowLength, step, fs)

signal = signal / max(abs(signal));

curPos = 1;

L = length(signal);


55

Air University Appendix A

H = hamming(windowLength);

m = ((fs/(2*windowLength))*[1:windowLength])';

C = zeros(numOfFrames,1);


window = H.*(signal(curPos:curPos+windowLength-1));

FFT = (abs(fft(window,2*windowLength)));

FFT = FFT(1:windowLength);

FFT = FFT / max(FFT);

C(i) = sum(m.*FFT)/sum(FFT);

if (sum(window.^2)<0.010)

C(i) = 0.0;

end


end

C = C / (fs/2);

)

C(i) = 0.0;

end


end

CODE OF SPECTRAL FLUX

function F = SpectralFlux(signal,windowLength, step, fs)


curPos = 1;

L = length(signal);


56



m = [0:windowLength-1]';

F = zeros(numOfFrames,1);


window = H.*(signal(curPos:curPos+windowLength-1));

FFT = (abs(fft(window,2*windowLength)));

FFT = FFT(1:windowLength);

FFT = FFT / max(FFT);

if (i>1)

F(i) = sum((FFT-FFTprev).^2);

else

F(i) = 0;

end


FFTprev = FFT;

end

CODE OF SPECTRAL ENTROPY

function En = SpectralEntropy(signal,windowLength,windowStep, fftLength, numOfBins);


curPos = 1;

L = length(signal);

numOfFrames = floor((L-windowLength)/windowStep) + 1;


En = zeros(numOfFrames,1);

h_step = fftLength / numOfBins;

57



window = (H.*signal(curPos:curPos+windowLength-1));

fftTemp = abs(fft(window,2*fftLength));

fftTemp = fftTemp(1:fftLength);

S = sum(fftTemp);

for (j=1:numOfBins)

x(j) = sum(fftTemp((j-1)*h_step + 1: j*h_step)) / S;

end

En(i) = -sum(x.*log2(x));

curPos = curPos + windowStep;

end

CODE OF SPECTRAL ROLL OFF

function mC = SpectralRollOff(signal,windowLength, step, c, fs)


curPos = 1;

L = length(signal);

numOfFrames = (L-windowLength)/step + 1;


m = [0:windowLength-1]';


window = (signal(curPos:curPos+windowLength-1));

FFT = (abs(fft(window,512)));

FFT = FFT(1:255);

totalEnergy = sum(FFT);

curEnergy = 0.0;

58


countFFT = 1;

while ((curEnergy<=c*totalEnergy) && (countFFT<=255))

curEnergy = curEnergy + FFT(countFFT);

countFFT = countFFT + 1;

end

mC(i) = ((countFFT-1))/(fs/2);


end

MATLAB CODE FOR COMPUTING STATISTICS

function FF = computeAllStatistics(fileName, win, step)

[x, fs] = wavread(fileName);

EE = Energy_Entropy_Block(x, win*fs, step*fs, 10);

E = ShortTimeEnergy(x, win*fs, step*fs);

Z = zcr(x, win*fs, step*fs, fs);

R = SpectralRollOff(x, win*fs, step*fs, 0.80, fs);

C = SpectralCentroid(x, win*fs, step*fs, fs);

F = SpectralFlux(x, win*fs, step*fs, fs);

FF(1) = statistic(EE, 1, length(EE), 'std');

FF(2) = statistic(Z, 1, length(Z), 'stdbymean');

FF(3) = statistic(R, 1, length(R), 'std');

FF(4) = statistic(C, 1, length(C), 'std');

59


FF(5) = statistic(F, 1, length(F), 'std');

FF(6) = statistic(E, 1, length(E), 'stdbymean');

MATLAB CODE FOR NEURAL NETWORK

CODE FOR TRAINING THE ANN

net1=newff(fv,t,20,{},'traingd');

net.trainParam.show = 50;

net.trainParam.lr = 0.01;

net.trainParam.epochs = 10000;

net.trainParam.goal = 0.01;

[net1,tr]=train(net1,fv,t);

CODE FOR SIMULATING THE ANN

clc

win=0.15;

step=0.1;

for i = 1:1

file = sprintf('%s%d.wav','c',i);

input('You have 1 seconds to say your name. Press enter when ready to record--> ');

y = wavrecord(44100,44100);

sound(y,44100);

wavwrite(y,44100,file);

60


end

load net1

file = sprintf('%s%u.wav','c',1);

ff1=computeAllStatistics(file, win, step);

ff1=ff1';

%fv(1:5,1)=ff1;

a=sim(net1,ff1);

b=a(1,1);

b

if(b>1)

rotaryfornechay

end

if(b<1)

rotaryforuper

end

61

Appendix C

Data Sheets

63

Air University Appendix C

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fyp final report_robotic arm

Documents

voice recognition voice

human voice

voice recognition system

voice command

pattern recognition

speech recognition review

sigmoid transfer function

linear transfer function