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