DIGITAL VOICE ENHANCEMENT

Under (digital signal processing)

Contents:

Abstract

What is DSP?

Analog and Digital signals

Signal Processing

Development of DSP

Digital Signal Processors (DSPs)

DSP Algorithm

Why DSP?

Related fields

Applications

Digital Voice Enhancement

Conclusions

References

ABSTRACT

Digital signal processing (DSP) is the

study of signals in a digital

representation and the processing

methods of these signals. DSP and

analog signal processing are subfields

of signal processing.

DSP includes sub fields like: audio and

speech signal processing, sonar and

radar signal processing, sensor array

processing, spectral estimation,

statistical signal processing, image

processing, signal processing for

communications, biomedical signal

processing, seismic data processing,

etc.

A digital signal processor (DSP) is a

specialized microprocessor designed

specifically for digital signal

processing, generally in real time.

Digital signal processing can be done

on general-purpose microprocessors.

However, a Digital signal processor

contains architectural optimizations to

speed up processing. These optimizers

are also important to lower costs, heat-

emission and power-consumption.

DSP technology is now a days

common place in such devices as

mobile phones, multimedia computers,

video recorders, CD players, hard disc

drive controllers and modems, and will

soon replace analog circuitry in TV

sets and telephones. An important

application of DSP is in Digital Voice

Enhancement.

DVE technology will enhance

communication in passenger vehicles,

but works especially well in vans and

sport utility vehicles where noise

levels are higher and the distance

between passengers in greater. The

system is also well suited for luxury

vehicles which are typically designed

to be quiet, but achieve that level of

quiet by using sound absorbing

materials, which also absorb speech.

What is DSP?

DSP, or Digital Signal Processing, as the

term suggests, is the processing of

signals by digital means. A signal in this

context can mean a number of different

things. Historically the origins of signal

processing are in electrical engineering,

and a signal here means an electrical

signal carried by a wire or telephone

line, or perhaps by a radio wave. More

generally, however, a signal is a stream

of information representing anything

from stock prices to data from a remote-

sensing satellite. The term "digital"

comes from "digit", meaning a number

(you count with your fingers - your

digits), so "digital" literally means

numerical; the French word for digital is

numerique. A digital signal consists of a

stream of numbers, usually (but not

necessarily) in binary form. The

processing of a digital signal is done by

performing numerical calculations.

Analog and digital signals

In many cases, the signal of interest is

initially in the form of an analog

electrical voltage or current, produced

for example by a microphone or some

other type of transducer. In some

situations, such as the output from the

readout system of a CD (compact disc)

player, the data is already in digital

form. An analog signal must be

converted into digital form before DSP

techniques can be applied. An analog

electrical voltage signal, for example,

can be digitized using an electronic

circuit called an analog-to-digital

converter or ADC. This generates a

digital output as a stream of binary

numbers whose values represent the

electrical voltage input to the device at

each sampling instant.

Signal processing

Signals commonly need to be processed

in a variety of ways. For example, the

output signal from a transducer may well

be contaminated with unwanted

electrical "noise". The electrodes

attached to a patient's chest when an

ECG is taken measure tiny electrical

voltage changes due to the activity of the

heart and other muscles. The signal is

often strongly affected by "mains

pickup" due to electrical interference

from the mains supply. Processing the

signal using a filter circuit can remove or

at least reduce the unwanted part of the

signal. Increasingly nowadays, the

filtering of signals to improve signal

quality or to extract important

information is done by DSP techniques

rather than by analog electronics.

Development of DSP

The development of digital signal

processing dates from the 1960's with

the use of mainframe digital computers

for number-crunching applications such

as the Fast Fourier Transform (FFT),

which allows the frequency spectrum of

a signal to be computed rapidly. These

techniques were not widely used at that

time, because suitable computing

equipment was generally available only

in universities and other scientific

research institutions.

Digital Signal Processors (DSPs)

The introduction of

the microprocessor in

the late 1970's and

early 1980's made

it possible for DSP techniques to be used

in a much wider range of applications.

However, general-purpose

microprocessors such as the

Intelx86 family are not ideally suited to

the numerically-intensive requirements

of DSP, and during the 1980's the

increasing importance of DSP led

several major electronics manufacturers

(such as Texas Instruments, Analog

Devices and Motorola) to develop

Digital Signal Processor chips -

specialized microprocessors with

architectures designed specifically for

the types of operations required in digital

signal processing. (Note that the

acronym DSP can variously mean

Digital Signal Processing, the term used

for a wide range of techniques for

processing signals digitally, or Digital

Signal Processor, a specialized type of

microprocessor chip). Like a general-

purpose microprocessor, a DSP is a

programmable device, with its own

native instruction code. DSP chips are

capable of carrying out millions of

floating point operations per second, and

like their better-known general-purpose

cousins, faster and more powerful

versions are continually being

introduced. DSPs can also be embedded

within complex "system-on-chip"

devices, often containing both analog

and digital circuitry.

DSP Algorithm

DSP algorithms have traditionally run

on specialized processors called digital

signal processors (DSPs). Algorithms

requiring more performance than DSPs

could provide were typically

implemented using application-specific

integrated circuit (ASICs). Today

however there are a number of

technologies used for digital signal

processing. These include more

powerful general purpose

microprocessors, field-programmable

gate arrays (FPGAs), digital signal

controllers (mostly for industrial apps

such as motor control), and stream

processors, among others.

Why DSP?

The world of science and engineering is

filled with signals: images from remote

space probes, voltages generated by the

heart and brain, radar and sonar echoes,

seismic vibrations, and countless other

applications. Digital Signal processing is

the science of using computers to

understand these types of data. This

includes a wide variety of goals:

filtering, speech recognition, image

enhancement data compression, neural

networks, and much more. DSP is one of

the most powerful technologies that will

shape science and engineering in the

twenty-first century.

Related fields

Automatic control

Computer Science

Data compression

Electrical engineering

Information theory

Telecommunication

Analog signal processing

Automatic control is the research area

and theoretical base for mechanization

and automation, employing methods

from mathematics and engineering. See

also control theory. A central concept is

that of the system which is to be

controlled, such as a rudder, propeller or

an entire ballistic missile. The systems

studied within automatic control are

mostly the linear systems. Automatic

control systems are composed of three

components:

Sensor(s), which measure some

physical state such as

temperature or liquid level.

Responder(s), which may be

simple electrical or mechanical

systems or complex special

purpose digital controllers or

general purpose computers.

Actuator(s), which effect a

response to the sensor(s) under

the command of the responder,

for example, by controlling a gas

flow to a burner in a heating

system or electricity to a motor in

a refrigerator or pump.

Computer science (or computing

science) is the study of the

theoretical foundations of

information and computation and

their implementation and application

in computer systems. Computer

science has many sub-fields; some

emphasize the computation of

specific results (such as computer

graphics), while others relate to

properties of computational

problems (such as computational

complexity theory). Still others focus

on the challenges in implementing

computations. For example,

programming language theory

studies approaches to describing

computations, while computer

programming applies specific

programming languages to solve

specific computational problems. A

further subfield, human-computer

interaction, focuses on the challenges

in making computers and

computations useful, usable and

universally accessible to people.

In computer science and information

theory, data compression or source

coding is the process of encoding

information using fewer bits (or

other information-bearing units) than

an unencoded representation would

use through use of specific encoding

schemes. One popular instance of

compression with which many

computer users are familiar is the

ZIP file format, which, as well as

providing compression, acts as an

archive, storing many files in a

single output file.

Electrical engineering —

sometimes referred to as electrical

and electronic engineering — is an

engineering field that deals with the

study and application of electricity,

electronics and electromagnetism.

The field first became an identifiable

occupation in the late nineteenth

century after commercialization of

the electric telegraph and electrical

power supply. The field now covers

a range of sub-studies including

power, electronics, control systems,

signal processing and

telecommunications.

Information theory is a branch of

applied mathematics and engineering

involving the quantification of

information. Historically,

information theory was developed to

find fundamental limits on

compressing and reliably

communicating data. Since its

inception it has broadened to find

applications in statistical inference,

networks other than communication

networks, biology (neurobiology),

quantum information theory, data

analysis, and other areas, although it

is still widely used in the study of

communication.

Telecommunication is the assisted

transmission of signals over a

distance for the purpose of

communication. In earlier times, this

may have involved the use of smoke

signals, drums, semaphore, flags, or

heliograph. In modern times,

telecommunication typically

involves the use of electronic

transmitters such as the telephone,

television, radio or computer. Early

inventors in the field of

telecommunication include

Alexander Graham Bell, Guglielmo

Marconi and John Logie Baird.

Telecommunication is an important

part of the world economy and the

telecommunication industry's

revenue has been placed at just under

3 percent of the gross world product.

Analog signal processing is any

signal processing conducted on

analog signals by analog means.

"Analog" indicates something that is

mathematically represented as a set

of continuous values. This differs

from "digital" which uses a series of

discrete quantities to represent

signal. Analog values are typically

represented as a voltage, electric

current, or electric charge around

components in the electronic

devices. An error or noise affecting

such physical quantities will result in

a corresponding error in the signals

represented by such physical

quantities.

Applications

The main applications of DSP are

audio signal processing, audio

compression, digital image

processing, video compression,

speech processing, speech

recognition, digital communications,

RADAR, SONAR, seismology, and

biomedicine. Specific examples are

speech compression and

transmission in digital mobile

phones, room matching equalization

of sound in Hifi and sound

reinforcement applications, weather

forecasting, economic forecasting,

seismic data processing, analysis and

control of industrial processes,

computer-generated animations in

movies, medical imaging such as

CAT scans and MRI, image

manipulation, high fidelity

loudspeaker crossovers and

equalization, and audio effects for

use with electric guitar amplifiers.

DIGITAL VOICE ENHANCEMENT

Digital Voice Enhancement (DVE™)

Technology Provides Effortless

Communication In Passenger Vehicles.

How DVE Works?

DVE, made possible by Digisonix

DVE™ voice enhancing technology

allows for comfortable and safe

conversations at highway speeds. The

Digisonix real-time DVE software uses

microphones in the vehicle cabin to

separate voice signals from the vehicle

noise. The DVE software then enhances

the voice signal and removes unwanted

noise to create a natural sounding

reproduction of the voice through the

vehicle’s audio system loudspeakers.

The DVE system can be combined with

voice recognition software to become

the gateway to voice command in an

Automobile.

Better Hands – Free Mobile

The DVE technology also provides a

clear voice signal to the hands-free

mobile phone and allows all passengers

to participate in the hands-free call. This

is a great feature for business meetings

or family calls. Included in the DVE

technology is the Digisonix voice

message system allowing passengers to

record a voice message hands-free in the

vehicle that can be played back later.

Designed for Vehicles

Digisonix DVE technology will enhance

communication in passenger vehicles but

works especially well in vans and sport

utility vehicles where noise levels are

higher and the distance between

passengers is greater. The system is also

well suited for luxury vehicles, which

are typically designed to be quiet by

using sound-absorbing materials that

also absorb speech.

• Enhanced passenger-to-passenger

voice communication

• All passengers participate in hands-free

phone calls

• Clear voice for mobile phone

• Voice recording in the vehicle

• Gateway to voice

recognition/commands systems

• Designed for all vehicles

• Easy to use

The First Automotive Implementation of

a Digital Voice Enhancement System

Applied Signal Processing, Inc. (ASP)

has applied its knowledge of vehicle

acoustics, adaptive modeling, and digital

signal processing to implement a Digital

Voice Enhancement™ (DVE) system for

Volkswagen AG. The system, which

uses a Texas Instrument

TMS320C55x™ DSP-based controller,

is offered as an optional feature to

enhance voice communication in the

new 2004 Volkswagen Multi van.

Minivans and sport utility vehicles are

tremendously popular, but these vehicles

have a greater distance between seated

passengers and higher interior noise

levels. Luxury vehicles typically

incorporate significant acoustic

treatments, which absorb road noise, but

also affect voice communication. These

characteristics often make normal

conversation among the vehicle

occupants difficult.

The ASP DVE system improves the

environment for natural conversations in

vehicles by using speech-enhancing

signal processing techniques to amplify

the voice signals, while minimizing the

amplification of other noises. Safety is a

direct benifit of the DVE system,

because the driver does not have to turn

his head or take his eyes off the road to

converse effectively with the other

passengers. Also, passengers are more

comfortable when they can speak in

normal tones and can hear others without

having to lean forward or change seats.

he DVE system uses microphones,

mounted overhead, to pick up the

occupants’ voices. It also deals with

non-speech inputs, such as road, wind,

engine, and accessory-generated noises.

The DVE system applies a

discriminating function to detect voice

activity from the dynamically changing

noise floor. The vehicle’s audio system

loudspeakers broadcast speech from one

zone to another within the vehicle,

Figure 1 (above). The DVE system

maintains high sound quality and speech

intelligibility by properly equalizing the

communication channel and integrating

vehicle-specific compensation routines

for volume and tone. It also addresses

classic feedback problems by removing

compensating for reverberations and

feedback for each of the talkers’

microphones. If the optional VW cell

phone car kit is installed the DVE

system becomes a digital hands-free

system in which all passengers can

participate in a phone call.

Other technical features of the DVE

system include smart-gating and a

variety of signal management tools that

compensate for voice levels, reception-

level requirements, and ambient noise

levels. Dynamic Gain Control increases

the total dynamic range, effectively

equalizing the sound levels of both loud

and soft talkers to increase speech

intelligibility and listener comfort.

CONCLUSIONS

Digital signal processing (DSP) is the

study of signals in a digital

representation and the processing

methods of these signals

Digital signal processing can be done

on general-purpose microprocessors.

By utilizing speech microphones,

standard audio loudspeakers with

amplification, and advanced digital

signal processing techniques, the ASP

DVE system allows for conversation

within vehicles at normal speech levels.

It provides an ideal way to acquire

speech signals, giving automotive

designers a new gateway for

implementing such features as a digital

voice notepad, voice recognition

systems, and hands-free cellular

telephony.

It can provide increased driver safety

and passenger comfort for a very

reasonable cost.

REFERENCES:

http://appliedsignalprocessing.com/

dve2.htm

http://appliedsignalprocessing.com/

dve.htm

http://www.wikipedia.org/

http://www.dsptutor.freeuk.com/

inro.htm