Share PDF

Search documents:
  Report this document  
    Download as PDF   
      Share on Facebook

SILENT SOUND TECHNOLOGY

A Project report submitted in partial fulfillment of

The requirement for the VIII SEMSETER PROJECT in

Bachelor of Computer Science Engineering

B.Tech

 

SESSION 2012-13

Submitted To

Submitted by:

Mr. Shivram Meena

Shalini Meru

(HOD of Computer

09ECACS037

Science Department)

January 2013

 

 

CHARTERED INSTITUTE OF TECHNOLOFY, ABU ROAD

Department of Computer Science

(An Initiative by Gyan Raman Charitable Trust) Mount Road, Abu Road-305002

1

ACKNOWLEDGEMENT

Dreams never turn into reality unless a lot of efforts and hard work is put into it. And no effort ever bears fruit in the absence of the support and guidance. It takes a lot of efforts to make your way to the goal and having someone to guide you and help you is always a blessing. I would like to take this opportunity to thank a few who were closely involved in the completion of this endeavor.

I am happy to take this opportunity to thank to people who helped me in the making of my seminar. I acknowledge the influence and inspiration of Mr. Shivram Meena Lecturer in Comuter Science Department who made the entire seminar an exciting and enjoyable experience.

At the outset, I thank God almighty for making my endeavor a success. I also express my gratitude to our Head of the Department, for providing me with adequate facilities, ways and means by which I was able to complete this seminar. I am also grateful to Mr. Krishnan MathUr, Mr. Shailendra and all other teachers of Computer Science Engineering Department for their invaluable teaching, help and support without which the successful completion of this seminar would not have

been possible.

2

ABSTRACT

Everybody has the experience of talking aloud in the cell phone in the midst of the disturbance while travelling in trains or buses. There is no need of shouting anymore for this purpose. ‘Silent sound technology’ is the answer for this problem.

The Silent sound technology is an amazing solution for those who had lost their voice but wish to speak over phone. It is developed at the Karlsruhe Institute of Technology and you can expect to see it in the near future. When demonstrated, it seems to detect every lip movement and internally converts the electrical pulses into sounds signals and sends them neglecting all other surrounding noise. It is definitely going to be a good solution for those feeling annoyed when other speak loud over phone.

‘Silent Sound’ technology aims to notice every movements of the lips and transform them into sounds, which could help people who lose voices to speak, and allow people to make silent calls without bothering others. Rather than making any sounds, your handset would decipher the movements your mouth makes by measuring muscle activity, then convert this into speech that the person on the other end of the call can hear. So, basically, it reads your lips. This new technology will be very helpful whenever a person looses his voice while speaking or allow people to make silent calls without listurbing others, even we can tell our PIN number to a trusted friend or relative without eavesdropping . At the other end, the listener can hear a clear voice. the awesome feature added to this technology is that "it is an instant polyglot" I.E, movements can be immediately transformed into the language of the user's choice. This translation works for languages like English, French & German. But, for the languages like Chinese, different tones can hold many different meanings. This poses Problem said Wand. he also said that in five or may be in ten years this will Be used in everyday's technology.

3

CONTENT

Page No.

1. INTRODUCTION………………………….……………….07

2.REQUIRMENT ANALYSIS ………………..….…….……..09 2.1. ORIGINATION…………………………….……..….…09

3. DESIGN AND CODING…..………………….….….…........10

3.1ELECTROMYOGRAPHY…………………….................10

3.2IMAGE PROCESSING………………………..….….......10

4ELECTROMYOGRAPHY…………………………………...11

4.1ELECTRICAL CHARACTERSTICS…………………….11

4.2PROCEDURE……………………………………………..12

4.3NORMAL RESULT……………………………………….13

4.4ABNORMAL RESULT……………………………………13

4.5EMG SIGNAL DECOMPOSITION………….………...…14

4.6APPLICATION OF EMG………………………………….14

4.7PREPROCESSING…………………………….………….14

4.8IMAGE ENHANCEMENT…………………………….......15

4.8.1IMAGE ENHANCEMENT TECHNIQUE………….....16

4.8.2FEATURE EXTRECTION……………………………..16

4.8.3GAUSSIAN STRETCH……………………..………….16

4.8.4PCA………………………………..……………………17

4.8.5DECORRELATION STRETCH………….…………....18

5.TESTING AND MAINTENENCE…………………..…. ........19

6.APPLICATION………………………………………………..21

7.CONCLUSION………………………………………………..22

8.SUMMARY……………………………………………….….. 23

9. SGGESTIONS FOR FUTURE WORK….……………….… 24

10. REFERENCES………………………………………………...25

4

LIST OF FIGURES

1.CHAPTER 1

1.1COMMON MAN TALKING AT SAME PLACE WITHOUT DISTURBANCE...7

2.CHAPTER 4

4.1ELECTROMYOGRAPHY SIGNAL GENERATION……………10

4.2ELECTROMYOGRAPHY INSTRUMENTS……………..………12

3.CHAPTER 5

5.1DIGITAL PROCESSING FLOWCHART ………………………..14

5

CHAPTER 1

INTRODUCTION

Silence is the best answer for all the situations …even your mobile understands!

The word Cell Phone has become greatest buzz word in Cellular Communication industry.

There are lots and lots of technology that tries to reduce the Noise pollution and make the environment a better place to live in.

I will tell about a new technology known as Silent Sound Technology that will put an end to Noise pollution.

General

You are in a movie theater or noisy restaurant or a bus etc where there is lot of noise around is big issue while talking on a mobile phone. But in the future this problem is eliminated with ”silent sounds”, a new technology unveiled at the CeBIT fair on Tuesday that transforms lip movements into a computer-generated voice for the listener at the other end of the phone.

It is a technology that helps you to transmit information without using your vocal cords . This technology aims to notice lip movements & transform them into a computer generated sound that can be transmitted over a phone . Hence person on other end of phone receives the information in audio.

In the 2010 CeBIT's "future park", a concept "Silent Sound" Technology demonstrated which aims to notice every movement of the lips and transform them into sounds, which could help people who lose voices to speak, and allow people to make silent calls without bothering others.

The device, developed by the Karlsruhe Institute of Technology (KIT), uses electromyography, monitoring tiny muscular movements that occur when we speak and converting them into electrical pulses that can then be turned into speech, without a sound uttered.

‘Silent Sound’ technology aims to notice every movements of the lips and transform them into sounds, which could help people who lose voices to speak, and allow people to make silent calls without bothering others. Rather than making any sounds, your handset would decipher the movements your mouth makes by measuring muscle activity, then convert this into speech that the person on the other end of the call can hear. So, basically, it reads your lips.

“We currently use electrodes which are glued to the skin. In the future, such electrodes might for example by incorporated into cellphones,” said Michael Wand, from the KIT.

6

Figure1.1-common people talking at same place without disturbance

The technology opens up a host of applications, from helping people who have lost their voice due to illness or accident to telling a trusted friend your PIN number over the phone without anyone eavesdropping — assuming no lip-readers are around.

Scope

Silent sound technology gives way to a bright future to speech recognition technology from simple voice commands to memorandum dictated over the phone all this is fairly possible in no isy public places.Without having electrodes hanging all around your face, these electrodes will be incorporated into cell phones .

It may have features like lip reading based on image recognition & processing rather than electromyography.Nano technology will be a mentionable step towards making the device handy.

The technology can also turn you into an instant polyglot. Because the electrical pulses are universal, they can be immediately transformed into the language of the user’s choice.

“Native speakers can silently utter a sentence in their language, and the receivers hear the translated sentence in their language. It appears as if the native speaker produced speech in a foreign language,” said Wand.The translation technology works for languages like English, French and Gernan, but for languages like Chinese, where different tones can hold many different meanings, poses a problem, he added.“We are also working on technology to be used in an office environment,” the KIT scientist told AFP.

The engineers have got the device working to 99 percent efficiency, so the mechanical voice at the other end of the phone gets one word in 100 wrong, explained Wand.“But we’re working to overcome the remaining technical difficulties. In five, maybe ten years, this will be useable, everyday technology,” .

7

CHAPTER 2

REQUIREMENT ANALYSIS

Silent Sound Technology will put an end to embarrassed situation such as-

An person answering his silent, but vibrating cell phone in a meeting, lecture or performance, and whispering loudly, ‘ I can’t talk to you right now’ .

In the case of an urgent call, apologetically rushing out of the room in order to answer or call the person back.

ORIGINATION:

Humans are capable of producing and understanding whispere speech in quiet environments at remarkably low signal levels. Most people can also understand a few unspoken words by lip-reading The idea of interpreting silent speech electronically or with a computer has been around for a long time, and was popularized in the 1968 Stanley Kubrick science- fiction film ‘‘2001 – A Space Odyssey ” A major focal point was the DARPA Advanced Speech Encoding Program (ASE ) of the early 2000’s, which funded research on low bit rate speech synthesis ‘‘with acceptable intelligibility, quality , and aural speaker recognizability in acoustically harsh environments

When you add lawnmowers, snow blowers, leaf blowers, jack hammers, jet engines, transport trucks, and horns and buzzers of all types and descriptions you have a wall of constant noise and irritation. Even when watching a television program at a reasonable volume level you are blown out of your chair when a commercial comes on at the decibel level of a jet.

The technology opens up a host of applications, from helping people who have lost their voice due to illness or accident to telling a trusted friend your PIN number over the phone without anyone eavesdropping — assuming no lip-readers are around.Native speakers can silently utter a sentence in their language, and the receivers hear the translated sentence in their language. It appears as if the native speaker produced speech in a foreign language.

8

CHAPTER 3

DESIGN & CODING

Silent Sound Technology is processed through some ways or methods. They are

Electromyography(EMG)

Image Processing

Electromyography :

The Silent Sound Technology uses electromyography, monitoring tiny muscular movements that occur when we speak.

Monitored signals are converted into electrical pulses that can then be turned into speech, without a sound uttered.

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles.

An electromyography detects the electrical potential generated by muscle cells, when these cells are electrically or neurologically activated.

Electromyographic sensors attached to the face records the electric signals produced by the facial muscles, compare them with pre recorded signal pattern of spoken words

When there is a match that sound is transmitted on to the other end of the line and person at the other end listen to the spoken words

Image Processing:

The simplest form of digital image processing converts the digital data tape into a film image with minimal corrections and calibrations.

Then large mainframe computers are employed for sophisticated interactive manipulation of the data.

In the present context, overhead prospective are employed to analyze the picture.

In electrical engineering and computer science, image processing is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.

9

CHAPTER 4

ELECTROMYOGRAPHY

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG is performed using an instrument called an electromyograph, to produce a record called an electromyogram. An electromyograph detects the electrical potential generated by muscle cells when these cells are electrically or neurologically activated. The signals can be analyzed to detect medical abnormalities, activation level, and recruitment order or to analyze the biomechanics of human or animal movement.

The Silent Sound Technology uses electromyography, monitoring tiny muscular movements that occur when we speak.

Monitored signals are converted into electrical pulses that can then be turned into speech, without a sound uttered.

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles.

 

An

electromyography

detects

the electrical

potential

generated

by

 

muscle cells, when these cells

are

electrically or

neurologically activated.

Figure-4.1: Electromorphography signal generation

ELECTRICAL CHARSTICSRACTE

The electrical source is the muscle membrane potential of about -90 mV. Measured EMG potentials range between less than 50 μV and up to 20 to 30mV, depending on the muscle under observation.

10

Typical repetition rate of muscle motor unit firing is about 7–20 Hz, depending on the size of the muscle (eye muscles versus seat (gluteal) muscles), previous axonal damage and other factors. Damage to motor units can be expected at ranges between 450 and 780 mV.

PROCEDURE:

There are two kinds of EMG in widespread use: surface EMG and intramuscular (needle and fine-wire) EMG. To perform intramuscular EMG, a needle electrode or a needle containing two fine-wire electrodes is inserted through the skin into the muscle tissue. A trained professional (such as a neurologist, physiatrist, or physical therapist) observes the electrical activity while inserting the electrode. The insertion activity provides valuable information about the state of the muscle and its innervating nerve. Normal muscles at rest make certain, normal electrical signals when the needle is inserted into them. Then the electrical activity when the muscle is at rest is studied. Abnormal spontaneous activity might indicate some nerve and/or muscle damage. Then the patient is asked to contract the muscle smoothly. The shape, size, and frequency of the resulting motor unit potentials are judged. Then the electrode is retracted a few millimeters, and again the activity is analyzed until at least 10–20 units have been collected. Each electrode track gives only a very local picture of the activity of the whole muscle. Because skeletal muscles differ in the inner structure, the electrode has to be placed at various locations to obtain an accurate study.

Figure4.2-: Electromyography instruments

Intramuscular EMG may be considered too invasive or unnecessary in some cases. Instead, a surface electrode may be used to monitor the general picture of muscle activation, as opposed

11

to the activity of only a few fibres as observed using an intramuscular EMG. This technique is used in a number of settings; for example, in the physiotherapy clinic, muscle activation is monitored using surface EMG and patients have an auditory or visual stimulus to help them know when they are activating the muscle (biofeedback).

Nerve conduction testing is also often done at the same time as an EMG to diagnose neurological diseases.

Some patients can find the procedure somewhat painful, whereas others experience only a small amount of discomfort when the needle is inserted. The muscle or muscles being tested may be slightly sore for a day or two after the procedure.

Normal results:

Muscle tissue at rest is normally electrically inactive. After the electrical activity caused by the irritation of needle insertion subsides, the electromyograph should detect no abnormal spontaneous activity (i.e., a muscle at rest should be electrically silent, with the exception of the area of the neuromuscular junction, which is, under normal circumstances, very spontaneously active). When the muscle is voluntarily contracted, action potentials begin to appear. As the strength of the muscle contraction is increased, more and more muscle fibers produce action potentials. When the muscle is fully contracted, there should appear a disorderly group of action potentials of varying rates and amplitudes (a complete recruitment and interference pattern).

Abnormal results:

EMG is used to diagnose diseases that generally may be classified into one of the following categories: neuropathies, neuromuscular junction diseases and myopathies.

Neuropathic disease has the following defining EMG characteristics:

An action potential amplitude that is twice normal due to the increased number of fibres per motor unit because of reinnervation of denervated fibres

An increase in duration of the action potential

A decrease in the number of motor units in the muscle (as found using motor unit number estimation techniques)

Myopathic disease has these defining EMG characteristics:

A decrease in duration of the action potential

A reduction in the area to amplitude ratio of the action potential

A decrease in the number of motor units in the muscle (in extremely severe cases only)

Because of the individuality of each patient and disease, some of these characteristics may not appear in every case.

12

EMG signal decomposition:

EMG signals are essentially made up of superimposed motor unit action potentials (MUAPs) from several motor units. For a thorough analysis, the measured EMG signals can be decomposed into their constituent MUAPs. MUAPs from different motor units tend to have different characteristic shapes, while MUAPs recorded by the same electrode from the same motor unit are typically similar. Notably MUAP size and shape depend on where the electrode is located with respect to the fibers and so can appear to be different if the electrode moves position. EMG decomposition is non-trivial, although many methods have been proposed.

Applications of EMG:

EMG signals are used in many clinical and biomedical applications. EMG is used as a diagnostics tool for identifying neuromuscular diseases, assessing low-back pain, kinesiology, and disorders of motor control. EMG signals are also used as a control signal for prosthetic devices such as prosthetic hands, arms, and lower limbs.

EMG can be used to sense isometric muscular activity where no movement is produced. This enables definition of a class of subtle motionless gestures to control interfaces without being noticed and without disrupting the surrounding environment. These signals can be used to control a prosthesis or as a control signal for an electronic device such as a mobile phone or PDA.

EMG signals have been targeted as control for flight systems. The Human Senses Group at the NASA Ames Research Center at Moffett Field, CA seeks to advance man-machine interfaces by directly connecting a person to a computer. In this project, an EMG signal is used to substitute for mechanical joysticks and keyboards. EMG has also been used in research towards a "wearable cockpit," which employs EMG-based gestures to manipulate switches and control sticks necessary for flight in conjunction with a goggle-based display.

Unvoiced speech recognition recognizes speech by observing the EMG activity of muscles associated with speech. It is targeted for use in noisy environments, and may be helpful for people without vocal cords and people with aphasia.

13

Figure5.2-Digital preprocessing

Pre-Processing:

Pre-processing consists of those operations that prepare data for subsequent analysis that attempts to correct or compensate for systematic errors.

Then analyst may use feature extraction to reduce the dimensionality of the data.

Thus feature extraction is the process of isolating the most useful components of the data for further study while discarding the less useful aspects.

It reduces the number of variables that must be examined, thereby saving time and resources.

Pre-processing consists of those operations that prepare data for subsequent analysis that attempts to correct or compensate for systematic errors. The digital imageries are subjected to several corrections such as geometric, radiometric and atmospheric, though all these correction might not be necessarily be applied in all cases. These errors are systematic and can be removed before they reach the user. The investigator should decide which pre- processing techniques are relevant on the basis of the nature of the information to be extracted from remotely sensed data. After pre-processing is complete, the analyst may use

14

feature extraction to reduce the dimensionality of the data. Thus feature extraction is the process of isolating the most useful components of the data for further study while discarding the less useful aspects (errors, noise etc). Feature extraction reduces the number of variables that must be examined, thereby saving time and resources.

Image Enhancement:

Improves the interpretability of the image by increasing apparent contrast among various features in the scene.

The enhancement techniques depend upon two factors mainly

The digital data (i.e. with spectral bands and resolution)

The objectives of interpretation

Common enhancements include image reduction, image rectification, image magnification, contrast adjustments, principal component analysis texture transformation and so on.

Image Enhancement operations are carried out to improve the interpretability of the image by increasing apparent contrast among various features in the scene. The enhancement techniques depend upon two factors mainly

The digital data (i.e. with spectral bands and resolution)

The objectives of interpretation

As an image enhancement technique often drastically alters the original numeric data, it is normally used only for visual (manual) interpretation and not for further numeric analysis. Common enhancements include image reduction, image rectification, image magnification, transect extraction, contrast adjustments, band ratioing, spatial filtering, Fourier transformations, principal component analysis and texture transformation.

INFORMATION EXTRACTION:

In Information Extraction the remotely sensed data is subjected to quantitative analysis to assign individual pixels to specific classes. It is then classified.

It is necessary to evaluate its accuracy by comparing the categories on the classified images with the areas of known identity on the ground.

The final result of the analysis consists of maps (or images), data and a report. Then these are converted to corresponding signals.

15

Information Extraction is the last step toward the final output of the image analysis. After pre-processing and image enhancement the remotely sensed data is subjected to quantitative analysis to assign individual pixels to specific classes. Classification of the image is based on the known and unknown identity to classify the remainder of the image consisting of those pixels of unknown identity. After classification is complete, it is necessary to evaluate its accuracy by comparing the categories on the classified images with the areas of known identity on the ground. The final result of the analysis consists of maps (or images), data and a report. These three components of the result provide the user with full information concerning the source data, the method of analysis and the outcome and its reliability.

Pre-Processing of the Remotely Sensed Images

When remotely sensed data is received from the imaging sensors on the satellite platforms it contains flaws and deficiencies. Pre-processing refers to those operations that are preliminary to the main analysis. Preprocessing includes a wide range of operations from the very simple to extremes of abstractness and complexity. These categorized as follow:

1.Feature Extraction

2.Radiometric Corrections

3.Geometric Corrections

4.Atmospheric Correction

The techniques involved in removal of unwanted and distracting elements such as image/system noise, atmospheric interference and sensor motion from an image data occurred due to limitations in the sensing of signal digitization, or data recording or transmission process. Removal of these effects from the digital data are said to be "restored" to their correct or original condition, although we can, of course never know what are the correct values might be and must always remember that attempts to correct data what may themselves introduce errors. Thus image restoration includes the efforts.

FEATURE EXTRACTION

Feature Extraction does not mean geographical features visible on the image but rather "statistical" characteristics of image data like individual bands or combination of band values that carry information concerning systematic variation within the scene. Thus in a multispectral data it helps in portraying the necessity elements of the image. It also reduces the number of spectral bands that has to be analyzed. After the feature extraction is complete the analyst can work with the desired channels or bands, but inturn the individual bandwidths are more potent for information. Finally such a pre-processing increases the speed and reduces the cost of analysis.

GAUSSIAN STRETCH

This method of contrast enhancement is base upon the histogram of the pixel values is called a Gaussian stretch because it involves the fitting of the observed histogram to a normal or Gaussian histogram. It is defined as follow:

16

F(x) = (a/p)0.5 exp(-ax2)

Multi-Spectral Enhancement Techniques

Image Arithmetic Operations

The operations of addition, subtraction, multiplication and division are performed on two or more co-registered images of the same geographical area. These techniques are applied to images from separate spectral bands from single multispectral data set or they may be individual bands from image data sets that have been collected at different dates. More complicated algebra is sometimes encountered in derivation of sea-surface temperature from multispectral thermal infrared data (so called split-window and multichannel techniques).

Band Subtraction Operation on images is sometimes carried out to co-register scenes of the same area acquired at different times for change detection.

Multiplication of images normally involves the use of a single'real' image and binary image made up of ones and zeros.

Band Rationing or Division of images is probably the most common arithmetic operation that is most widely applied to images in geological, ecological and agricultural applications of remote sensing. Ratio Images are enhancements resulting from the division of DN values of one spectral band by corresponding DN of another band. One instigation for this is to iron out differences in scene illumination due to cloud or topographic shadow. Ratio images also bring out spectral variation in different target materials. Multiple ratio image can be used to drive red, green and blue monitor guns for color images. Interpretation of ratio images must consider that they are "intensity blind", i.e, dissimilar materials with different absolute reflectances but similar relative reflectances in the two or more utilised bands will look the same in the output image.

Principal Component Analysis(PCA):

Spectrally adjacent bands in a multispectral remotely sensed image are often highly correlated. Multiband visible/near-infrared images of vegetated areas will show negative correlations between the near-infrared and visible red bands and positive correlations among the visible bands because the spectral characteristics of vegetation are such that as the vigor or greenness of the vegetation increases the red reflectance diminishes and the near-infrared reflectance increases. Thus presence of correlations among the bands of a multispectral image implies that there is redundancy in the data and Principal Component Analysis aims at removing this redundancy.

Principal Components Analysis (PCA) is related to another statistical technique called factor analysis and can be used to transform a set of image bands such that the new bands (called principal components) are uncorrelated with one another and are ordered in terms of the amount of image variation they explain. The components are thus a statistical abstraction of the variability inherent in the original band set.

To transform the original data onto the new principal component axes, transformation coefficients (eigen values and eigen vectors) are obtained that are further applied in alinear

17

fashion to the original pixel values. This linear transformation is derived from the covariance matrix of the original data set. These transformation coefficients describe the lengths and directions of the principal axes. Such transformations are generally applied either as an enhancement operation, or prior to classification of data. In the context of PCA, information means variance or scatter about the mean. Multispectral data generally have a dimensionality that is less than the number of spectral bands. The purpose of PCA is to define the dimensionality and to fix the coefficients that specify the set of axes, which point in the directions of greatest variability. The bands of PCA are often more interpretable than the source data.

Decor relation Stretch:

Principal Components can be stretched and transformed back into RGB colours - a process known as decorrelation stretching.

If the data are transformed into principal components space and are stretched within this space, then the three bands making up the RGB color composite images are subjected to stretched will be at the right angles to each other. In RGB space the three-color components are likely to be correlated, so the effects of stretching are not independent for each color. The result of decorrelation stretch is generally an improvement in the range of intensities and saturations for each color with the hue remaining unaltered. Decorrelation Stretch, like principal component analysis can be based on the covariance matrix or the correlation matrix. The resultant value of the decorrelation stretch is also a function of the nature of the image to which it is applied. The method seems to work best on images of semi-arid areas and it seems to work least well where the area is covered by the imaging includes both land and sea.

Fourier Transformation

The Fourier Transform operates on a single -band image. Its purpose is to break down the image into its scale components, which are defined to be sinusoidal waves with varying amplitudes, frequencies and directions. The coordinates of two-dimensional space are expressed in terms of frequency (cycles per basic interval). The function of Fourier Transform is to convert a single-band image from its spatial domain representation to the equivalent frequency-domain representation and vice-versa.

The idea underlying the Fourier Transform is that the grey-scale valuea forming a single-band image can be viewed as a three-dimensional intensity surface, with the rows.

18

CHAPTER 6

Testing & Maintenance

The general outline of the white box testing process is as follows:

1.Perform risk analysis to guide the whole testing process. In Microsoft’s SDL process, this is referred to as threat modeling [Howard 06].

2.Develop a test strategy that defines what testing activities are needed to accomplish testing goals.

3.Develop a detailed test plan that organizes the subsequent testing process.

4.Prepare the test environment for test execution.

5.Execute test cases and communicate results.

Why testing is done

•Testing is the process of running a system with the intention of finding errors.

•Testing enhances the integrity of a system by detecting deviations in design and errors in the system Testing aims at detecting error-prone areas.

•Testing also add value to the product by confirming to the user requirements.

Testing Principles

To discover as yet undiscovered errors.

•All tests should be traceable to customer’s requirement.

•Tests should be planned long before the testing actually begins.

•Testing should begin “in the small” & progress towards “testing in the large”. Exhaustive Testing is not possible.

•To be most effective training should be conducted by an Independent Third Party

Testing Objectives

Testing is a process of executing a program with the intent of finding errors.

•A good test case is one that has a high probability of finding an as yet undiscovered error.

•A successful test is one that uncovers an as yet undiscovered error.

6.Kinds of Testing

6.1Black Box Testing- Not based on any knowledge of internal designs or code. Tests

are based on requirements and functionality.

6.2 White Box Testing- Based on the knowledge of the internal logic of an application’s code. Tests are based on coverage of code statements, branches, paths.

Unit Testing- The most ‘micro’ scale of testing; to test particular functions and code modules. Typically done by the programmer and not by the testers, as it requires detailed knowledge of the internal program design and code. Not always easily done unless the application has a well-designed architecture with tight code; may require developing test driver modules or test harnesses.

Integration Testing- Testing of combined parts of an application to determine if they

function together correctly. The ‘parts’ can be code modules, individual applications, client and server applications on a network, etc. This type of testing is especially relevant to client/

19

server and distributed systems.

Functional Testing- Black-box type testing geared to functional requirements of an application; testers should do this type of testing. This doesn’t mean that the programmers shouldn’t check that their code works before releasing it. specifications; covers all combined parts of the system.

Regression Testing- Re-testing after fixes or modifications of the software or its environment. It is difficult to determine how much re testing is needed, especially near the end of the development cycle. Automated testing tools can be especially useful for this type of testing.

Acceptance Testing- Final testing based on the specifications of the end user or customer or based on use by end-users/ customers over some limited period of time.

User Acceptance Testing- Determining if software is satisfactory to an end user customer.

20

CHAPTER 7

APPLICATIONS:

The Technology opens up a host of application such as mentioned below :

Helping people who have lost their voice due to illness or accident.

Telling a trusted friend your PIN number over the phone without anyone eavesdropping assuming no lip-readers are around.

Silent Sound Techniques is applied in Military for communicating secret/confidential matters to others.

Native speakers can silently utter a sentence in their language, and the receivers can hear the translated sentence in their language. It appears as if the native speaker produced speech in a foreign language. The translation technology works for languages like English, French and German, except Chinese, where different tones can hold many different meanings.

Allow people to make silent calls without bothering others.

21

CHAPTER 9

CONCLUSION

Thus Silent Sound Technology,one of the recent trends in the field of information technology implements ”Talking Without Talking”.

It will be one of the innovation and useful technology and in mere future this technology will be use in our day to day life.

‘Silent Sound’ technology aims to notice every movements of the lips and transform them into sounds, which could help people who lose voices to speak, and allow people to make silent calls without bothering others. Rather than making any sounds, your handset would decipher the movements your mouth makes by measuring muscle activity, then convert this into speech that the person on the other end of the call can hear. So, basically, it reads your lips.

Engineers claim that the device is working with 99 percent efficiency.

It is difficult to compare SSI technologies directly in a meaningful way. Since many of the s stems are still preliminary, it would not make sense, for example, to compare speech recognition scores or synthesis quality at this stage.

With a few abstractions, however, it is possible to shed light on the range of applicability and the potential for future commercialization of the different methods

22

CHAPTER 10

SUMMARY

In a way of summarization ‘Silent Sound’ technology aim to notice every movements of the lips and transform them into sounds, which could help people who lose voices to speak, and allow people to make silent calls without bothering others. Rather than making any sounds, your handset would decipher the movements your mouth makes by measuring muscle activity, then convert this into speech that the person on the other end of the call can hear. So, basically, it reads your lips.

Since many of the systems are still preliminary, it would not make sense, for example, to compare speech recognition scores or synthesis quality at this stage.

With a few abstractions, however, it is possible to shed light on the range of applicability and the potential for future commercialization of the different methods

23

CHAPTER 11

SUGGESTIONS FOR FUTURE WORK

The technology can also turn you into an instant polyglot. Because the electrical pulses are universal, they can be immediately transformed into the language of the user’s choice.

“Native speakers can silently utter a sentence in their language, and the receivers hear the translated sentence in their language. It appears as if the native speaker produced speech in a foreign language,” said Wand.

The translation technology works for languages like English, French and Gernan, but for languages like Chinese, where different tones can hold many different meanings, poses a problem, he added.“We are also working on technology to be used in an office environment,” the KIT scientist told AFP.

The engineers have got the device working to 99 percent efficiency, so the mechanical voice at the other end of the phone gets one word in 100 wrong, explained Wand.

“But we’re working to overcome the remaining technical difficulties. In five, maybe ten years, this will be useable, everyday technology,” .

24

CHAPTER 10

REFERENCES

1.www.google.com

2.www.slideshare.net

3.www.techpark.net

4.www.telecomspace.com

5.www.wikipedia.com

25