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levels. At the same time, the image information can be

compressed or it can be processed into a template to

reduce storage requirements.

Vascular biometric technologies are being used or

proposed for many applications. Some of these include

access control to secure areas, employee time-clock

tracking, Automatic Teller Machines (ATMs), secure

computer login, person identiﬁcation, and as one of

several biometrics in mu lti-biometric systems. The

technolog y is not appropriate for certain other appli-

cations such as criminal forensics or surveillance.

Currently, however, little vascular biometric image

information is being exchanged between the equip-

ment and devices from different vendors. This is due

in part to the lack of standards relating to interopera-

bility of vascular biometric technology. In the general

area of biometrics interoperability, the International

Standard Or ganization (ISO) and the regional organi-

zations, such the INCITS M1 group in the US, deﬁne a

collection of standards relating to the various biomet-

ric modalities that include data interchange formats,

conformance testing of image and template inter-

change formats, performance testing and application

proﬁles. The most critical are the formats for infor-

mation exchange that would ensure interoperability

among the various vendors. Deﬁnition and standardi-

zation of the data structures for the interoperable use

of biometric data among organizations is addressed in

the ISO/IEC 19794 series [2], which is the multipart

biometric data interchange format standard, which

describes standards for capturing , exchanging, and

transferring different biometric data from personal

characteristics such as voice, or properties of parts of

the body like face, iris, ﬁngerprint, hand geometry, or

vascular patterns.

To address this short-coming in the vascula r do-

main, the ISO has published a standard for a vascular

biometric image interface format, entitled the ISO/IEC

19794-9 (Biometric data interchange format – part 9

Vascular image format) [3].

The main purpose of thi s standard is to deﬁne a

data record format for storing and transmitting vascu-

lar biometric images and certain of their attributes

for applications requiring the exchange of raw or

processed vascular biometric images. It is intended

for applications not severely limited by the amount of

storage required and is a compromise or a trade-off

between the resources required for data storage or

transmission and the potential for improved data

quality/accuracy. Basically, it enables various prepro-

cessing or matching algorithms to identify and verify

the type of vascular biometric image data transferred

from other image sources and to allow operations on

the data. The currently available vascular biometric

technologies that are commercialized and that may

utilize this standard for image exchange are technolo-

gies that use the back of the hand, the palm, and the

ﬁnger [4–6]. There is the ability to extend the standard

to accommodate other portions of the body if the

appropriate technology is brought forward.

The use of standardized source images can provide

interoperability among and between vendors relying

on various different recognition or veriﬁcation algo-

rithms. Moreover, the format standard will offer the

developer more freedom in choosing or combining

matching algorithm technology. This also helps appli-

cation developers focus on their application domain

without concern about variations in how the vascular

biometric data was processed in the vascular biometric

modalities.

Introduction to ISO/IEC 19794-9 Vascular

Image Data Format Standa rd

ISO published the ISO/IEC 19794-9 Vascular Image

Data Format Standard in 2007, as a part of the ISO/

IEC 19794 series. The ISO/IEC 19794-9 vascular image

data format standard speciﬁes an image interchange

format for biometric person identiﬁcation or veriﬁca-

tion technologies that utilize human vascular biometric

images and may be used for the exchange and compari-

son of vascular image data [7]. It speciﬁes a data record

format for storing, recording, and transmitting vascu-

lar biometric information from one or more areas

of the human body. It deﬁnes the contents, format,

and units of measurement for the image exchange. The

format consists of mandatory and optional items, in-

cluding scanning parameters, compressed or uncom-

pressed image speciﬁcations, and vendor-speciﬁc

information.

The ISO/IEC 19794-9 vascular image data format

standard describes the data interchange format for

three different vascular bio metric technologies utiliz-

ing different parts of the hand including back-of-hand,

ﬁnger, and palm. The standard also supports room for

extension to other vascular biometrics on other parts

of the human body, if needed. Figure 1 shows an

1370

Vascular Image Data Format, Standardization

example of vascular biometric areas on different parts

of the hand that are speciﬁed in ISO/IEC 19794-9.

The interchange format follows the standard data

conventions of the 19794 series of standards such as

requiring all multi-byte data to be in big-endian for-

mat, transmitting the most signiﬁcant byte ﬁrst and

the least signiﬁcant byte last, and with in a byte, the

order of transmission shall be the most signiﬁcant bit

ﬁrst and the least signiﬁcant bit last. All numeric values

are treated as unsigned integers of ﬁxed-length.

The vascular pattern biometric technologies cur-

rently available employ images from the ﬁnger, back of

the hand, and palm side of the hand. The location used

for imaging is to be speciﬁed in the format. To further

specify the locations, the object (target body) coordi-

nate system for each vascular technology is deﬁned.

Standard poses and object coordinate systems are also

deﬁned. All the coordinate systems are right-handed

Euclidian coordinate systems. It is then possible to

optionally specify a rotation of the object from the

standard pose. In order to map the object coordinate

system to the image coordinate system without further

translation, an x- and y-axis origin for scanning can be

speciﬁed in the data.

The image is acquired by scanning a rectangular

region of interest of a human body from the upper left

corner to the lower right in raster scan order, that is,

along the x-axis from top to bottom in the y direction.

The vascular image data can be stored either in a raw or

compressed format. In a raw format, the image is

represented by a rectangular array of

▶ pixels with

speciﬁed numbers of columns and rows. Images can

also be stored using one of the speciﬁed lossless or lossy

compression methods, resulting in compressed image

data. The allowable compression methods include the

JPEG [8], JPEG2000 [9], and JPEG LS [10]. It is

recommended that the compression ratio be less than

a factor of 4:1 in order to maintain a quality level

necessary for further processing.

Image capture requirements are dependent on var-

ious factors such as the type of application, the avail-

able amount of raw pixel information to be retained or

exchanged, and the targeted performance. Another

factor to consider as a requirement for vascular bio-

metric imaging is that the physical size of the target

body area where an application captures an image for

the extraction of vascular pattern data may vary sub-

stantially (unlike other biometric modalities).

The image capture requirements also deﬁne a set of

additional attr ibutes for the capture devices such as

▶ gray scale depth, ▶ illumi nation source, horizontal

and vertical resolution (in pixels per cm), and the

aspect ratio. For most of the available vascular biomet-

ric technologies, the gray scale depth of the image

ranges up to 128 gray scale levels, but may, if required,

utilize two or more bytes per gray scale value instead of

one. The illu mination sources used in a ty pical vascu-

lar biometric system are near-infrared wavelengths in

the range of approximately 700–1200 nm infrared light

sources. However, near-infrared, mid-infrared, and

Vascular Image Data Format, Standardization. Figure 1 Examples of vascular biometric areas on different parts of the

hand [3].

Vascular Image Data Format, Standardization

1371

visible light sources can be deﬁned and more than one

source may be employed.

Table 1 shows the basic structure of the vascular

image biometric data block. A single data block starts

with a vascular image record header, which contains

general information about the data block such as the

identiﬁcation of the image capture device and the

format version. One or more vascular image blocks

follow the record header. Each image block consists

of an image header and raw or compressed image data.

The image header contains all the image speciﬁc infor-

mation such as the body location, rotation angle, and

imaging conditions. All images in a data block must

come from the same capture device. If multiple devices

are used, then multiple blocks must be used.

The vascular image record header consist of general

information on the vascular images contained in the

data block, such as the format version number, total

length of the record block, capture device identiﬁca-

tion, and the number of images contained in the data

block. More speciﬁc information includes format iden-

tiﬁer, version number, record length, capture dev ice

ID, and number of images.

For each image in the data block, the vascular

image header describes individual image-speciﬁc in-

formation incl uding image type, vascular image record

length, image width and height, gray scale depth,

image position, property bit ﬁeld, and rotation angle.

Other information in the vascular image header may

include Image format, illumination type, image back-

ground, horizontal scan resolution, vertical scan reso-

lution, pixel aspect ratio, and vascular image header

constants. The image data follows and is used to store

the biometric image information in the speciﬁc format

deﬁned in the vascular image record header.

Future Activities

There are considerable ongoing standardization activ-

ities relating to vascular biometrics, building upon the

biometric data interchange format for vascular images

standard. A companion document that speciﬁes the

conformance testing for the data interchange format

is currently under development. The conformance

standard speciﬁes how to check whether the data pro-

duced by a vascular imaging device, does indeed agree

with the interchange format, as well as which items are

mandatory or optional. There are also ongoing efforts,

both internationally and in the U.S., to include the

vascular image formats into the various applicati on

proﬁles (such as the INCITS M1 Proﬁle for Point-

of-Sale Biometric Identiﬁcation/Veriﬁcation), which

deﬁne how to use vascular biometrics in the speciﬁc

context of an application. There are also efforts at

including vascular methods in multi-biometric fusion

schemes or as a biometric component of a smart-card

based solution. Eventually, it is expected that vascular

methods will become one of the important biometric

modalities, offering beneﬁts not provided by the other

techniques in cer tain applications.

Summary

Vascular biometric technologies including vascular

images from the back-of-hand, ﬁnger, and palm are

being used as a security integrated solution in many

applications. The need for ease of exchanging and

transferring vascular biometric data from biometric

recognition devices and applications or between differ-

ent biometric modalities requires the deﬁnition of a

Vascular Image Data Format, Standardization. Table 1 Vascular image biometric data block

Bytes Type Content Description

1–26 Data block header Header used by all vascular biometric image providers.

Information on format version, capture device ID, number of

vascular images contained in the data block, etc.

27–58 Vascular image header Image header for the first image. Contains all individual image

specific information

Unsigned char Image data



Vascular image header Image header for the last image

Unsigned char Image data

1372

Vascular Image Data Format, Standardization

vascular biometrics data format standard. The devel-

opment of the vascular biometric data interchange

format standard also helps to ensure interoperability

among the various vendors. This paves the pathway so

that vascular biometric technolo gies can be adopted

as a standard security technology which is easily in-

tegrated in various ranges of applications.

Related Entries

▶ Back-of-hand Vein

▶ Finger Data Interchange Format Standardization

▶ Finger Vein

▶ Palm Vein

▶ Vein and Vascular Recognition

References

1. Choi, A.H., Tran, C.N.: Handbook of Biometrics: Hand Vascular

Pattern Recognition Technology. Springer, New York (2007)

2. ISO/IEC 19794-1 Information Technology: Biometric Data

Interchange Format – Part 1: Framework/reference model

3. ISO/IEC 19794-9 Information Technology: Biometric Data

Interchange Format – Part 9: Vascular image data

4. Im, S.K., Park, H.M., Kim, Y.W., Han, S.C., Kim, S.W., Kang,

C.H.: Biometric identiﬁcation system by extracting hand vein

patterns. J. Korean Phy. Soc. 38(3), 268–272 (2001)

5. Miura, N., Nagasaka, A., Miyatake, T.: Feature Extraction of

Finger-Vein Patterns Based on Repeated Line Tracking and Its

Application to Personal Identiﬁcation. Mach. Vis. Appl. 15,

194–203 (2004)

6. Watanabe, M., Endoh, T., Shiohara, M., Sasaki, S.: Palm vein

authentication technology and its applications. In: Proceedings of

Biometric Consortium Conference, VA, USA, September 2005

7. Volner, R., Bores, P.: Multi-Biometric techniques, standards

activities and experimenting. In: Baltic Electronics Conference,

pp. 1–4. Tallinn, Estonia (2006)

8. ISO/IEC 10918 (all parts) Information Technology: Digital

Compression and Coding of Continuous Tone Still Images

9. ISO/IEC 15444 (all parts) Information Technology: JPEG 2000

Image Coding System

10. ISO/IEC 14495 (all parts) Information Technology: Lossless and

Near-Lossless Compression of Continuous Tone Still Images

Vascular Network Pattern

The network pattern composed of blood vessels.

Human blood vessels develop network structures in

each level of artery, arteriole, capillary, venule, and

vein. The network of major blood vessels can be seen

in funduscopy and in visual observation of body sur-

face. The vascular networks in fundus image are those

of retinal arteries and retinal veins. The blood vessels

observed on body surface are the cutaneous veins.

Both network patterns can be used in biometric

authentication. There are no apparent evidence on

the uniqueness and the permanen ce of the vascular

network pattern. However, in practice, the vascular

pattern has been used for biometric authentication

without a serious problem. Since the retinal pattern is

kept inside an eye, it is stable and seldom affected by

the change of outer environment. It is not easily ob-

servable by others and robust against the theft and the

forgery. The retinal pattern is complex, and high iden-

tiﬁcation accuracy can be expected. The authentication

using this retinal pattern has been used in the institu-

tions that require high level of security.

The vascular network pattern in a hand and in a

ﬁnger can be visualized by transilluminati on imaging

or reﬂection-type imaging using near-infrared light.

The authentication with vascular pattern of a hand

and a ﬁnger is safer and more convenient than that

with retinal pattern. It has been used in common

security applications such as the authentication in

ATM and in access management.

▶ Performance Evaluation, Overview

Vascular Recognition

▶ Retina Recognition

Vector Quantization

The vector quantization (VQ) is a process of mapping

vectors from a large vector space to a ﬁnite number of

regions in that space (Linde, Y., Buzo, A., Gray, R.: An

algorithm for vector quantizer design. IEEE Trans.

Vector Quantization

1373

Comm. 28, 84–9517 (1980)). Each region is called

a cluster and can be represented by its center called a

codeword. The collection of all codewords is called

a codebook. During the training phase, a speaker-

speciﬁc VQ codebook is generated for each known

speaker by clustering the corresponding training acous-

tic vectors. The distance from a vector to the closest

codeword of a codebook is called a VQ-distortion.

During the recognition phase, an input utterance of

an unknown voice is vector-quantized using each

trained codebook, outputting a VQ distortion for

each codebook, each client speaker. The speaker

corresponding to the VQ codebook with the smallest

distortion is identiﬁed. Both for the training and test-

ing phases, the VQ process works indepe ndently on

each input frame and produces an averaged result (a

codebook or VQ distortion). Thus, there is no need to

perform a time alignment. The lack of time warping

greatly simpliﬁes the system; however, it neglects

speaker-dependent temporal information that may be

present in prompted phrases.

▶ Speaker Matching

Vein

Veins are the blood vessels that carry blood to the

heart. In the cardiovascular system, blood vessels con-

sist of arteries, capillaries, and veins. Veins collect

blood from capillaries and carry it toward the heart.

In most of the veins, the blood is deoxygenated. The

pulmonary vein is one of the exceptions that carry

oxygenated blood. The walls of veins are relatively

thinner and less elastic than those of arteries. Some

veins have one-way ﬂaps called venous valves that

prevent blood from ﬂowing back. The valves are

found in the veins that carry blood against the force

of gravity, especially in the veins of the lower

extremities.

The vein in the subcutaneous tissue is called a

cutaneous vein. Some of the cutaneous veins can be

observed on the body surface w ith the naked eye. With

the light of high transmission through body tissue such

as near-infrared light, we can obtain a clear image of

the cutaneous vein. Since the pattern of venous

network is largely different between individuals, the

images can be used for authentication. The biometric

authentication using the venous network patterns in a

palm and a ﬁnger is common.

▶ Palm Vein Image Sensor

▶ Performance Evaluation, Overview

Vein Biometrics

▶ Vascular Image Data Format, Standardization

Vein Recognition

▶ Retina Recognition

Velocity (Speed)

Velocity of pen movement during the signing process.

Velocity features seem to be one of the most useful

features of on-line signatures. Generally, velocity is com-

puted from the ﬁrst-order derivative of the pen position

signal with respect to time. The easiest way to compute

the velocity is to calculate the distance between two

consecutive pen-tip positions if the data is acquired at

equidistant sample points. Velocity features are repre-

sented in two ways: velocities along the x-axis and y-axis

or velocity along the pen movement direction (tangen-

tial direction). In the latter case, the direction of pen

movement is also considered as a separate feature.

▶ Signature Recognition

Verification

Biometric veriﬁcation is a process that shows true or

false a claim about the similarity of biometric reference(s)

1374

Vein

and recognition biometric sample(s) by making a bio-

metric comparison(s).

▶ Verifica tion/Identification/Authentication/Recogni-

tion: The Terminology

Vetting

▶ Background Checks

Video Camera

▶ Face Device

Video Surveillance

▶ Human Detection and Tracking

Video-based Face Recognition

▶ Face Recognition, Video-based

Video-based Motion Capture

▶ Markerless 3D Human Motion Capture from Images

Visible Spectrum

Synonyms

Optical spectrum; Visible light

Definition

The portion of the electromagnetic spectrum that is

visible (detected) by the human eye. The wavelengths

for this spect rum is 380 to 750 nm, which are the

wavelengths seen (detected) by the human eye in air.

▶ Iris Databases

Visual Memory

Visual memory is the perceptual ability that allows

visual images to remain in memory after they are no

longer visible. It supports the matching process be-

tween two ﬁngerprints when eye movements are

required.

▶ Latent Fingerprint Experts

Visual Sensor

▶ Face Device

Visual-dynamic Speaker Recognition

▶ Lip Movement Recognition

Visual-dynamic Speaker Recognition

1375

Vitality

▶ Liveness Detection: Fingerprint

▶ Liveness Detection: Iris

Viterbi Algorithm

The Viterbi algorithm is the conventional, recursive,

efﬁcient way to decode a Hidden Markov Model that is

to ﬁnd the optimal state sequence, given the observa-

tion sequence and the model. It provides information

about the hidden process and is a good an efﬁcient

approximation of the evaluation problem.

▶ Hidden Markov Models

VOCs (Volatile Organic Compounds)

Organic chemicals that have a high vapor pressure

resulting in a relatively high abundance in the head-

space of samples.

▶ Odor Biometrics

Voice Authentication

Voice authentication is also known as speaker au-

thentication, speaker veriﬁcation, and one-to-one

speaker recognition. For example, for a client –a

bank customer – to be authenticated, the client must

ﬁrst go through an enrollment procedure, also known

as training. During enrollment, the client provides a

number of voice samples to the system, which in turn

are used to build a voice model for the client. When

requesting a voice authentication, a client must ﬁrst

announce his or her identity. This may be done verbal-

ly by saying name, user id, account number or the

like, or it may be done by presenting an identifying

token such as a staff card or bank card. Then the

authentication takes place when the person speaks a

set phra se or a requested phrase or simply engages in

a dialogue with the authentication system. If the

voice sample matches the stored model or template

of the claimed identity, the client is authenticated.

If an impostor tries to be authenticated as a particular

client, the impostor’s voice will not match the client

model and the impostor will be rejected. The authen-

tication paradigm only compares a speech sample

with a single client model, namely the model of

the claimed identity. Hence, it is sometimes known as

one-to-one speaker recognition. In contrast speaker

identiﬁcation compares a speech sam ple with every

possible client model, to ﬁnd the closest match.

Hence this paradigm is also known as one-to-many

speaker recognition.

▶ Liveness Assurance in Voice Authentication

▶ Speaker Recognition Standardization

Voice Biometric

▶ Speaker Recognition, Overview

Voice Biometric Engine

▶ Speaker Matching

1376

Vitality

Voice Device

DOROTEO T. TOLEDANO,

OAQU I N GONZALEZ-RODRIGUEZ,

AVI E R ORTE G A-GARCIA

ATVS – Biometric Recognition Group, Escuela

Politecnica Superior, Universidad Autonoma de

Madrid, Spain

Synonyms

Microphone; Speech input device

Definition

Voice dev ice in the context of biometrics is frequently

used as a synonym for a simpler word: microphone.

A microphone [1] is a transducer that converts sound

(or equivalently, air pressure varia tions) into electrical

signals. There are many different types of microphones

that use different metho ds to achieve this transduction,

most of which will be revised in this article. Besides

the method employed to do the transduction, micro-

phones are most frequently encapsulated, and the encap-

sulation allows to build microphones with different

directional characteristics, which allow, for instance, to

capture the voice coming from one direction and reject

(to a certain extent) the noises or voices coming from

other directions. Apart from the directionality, micro-

phones also have different frequency responses, sensitiv-

ities, and dynamic ranges. All these characteristics can

dramatically inﬂuence the performance of a speech bio-

metric system, and should therefore be taken into ac-

count in the design of such systems.

Microphones are the most commonly used speech

input devices, and for that reason they deserve most of

the space of this article. However, this article will be

incomplete without mentioning that microphones, at

least traditional microphones, are not the only speech

input device that can be used in speech biometrics.

For instance, microphones may be arranged to form

▶ microphone arrays. There also exist special micro-

phones called

▶ contact microphones that transduce

vibrations in solid bodies into electrical signals. Finally,

there is also the possibility of combining the acoustic

evidence and the visual evidence of speech by record-

ing the audio and also the movement of the lips in

what is commonly referred to as audio-visual speech

processing. Deﬁnitional entries are devoted at the end

of this article to these special speech input devices.

The ﬁrst step in any voice biometric (or automatic

speaker recognition) system is to capture the voice of

the speaker, and speech input devices are used for this

purpose.

Introduction

The human hearing sense is extremely robust against

noise and small distortions in the speech and humans

are very good at recognizing people based on their

voices, even under strong distortion and noise. Most

speech input devices are desig ned with the goal of

capturing speech or music, translatin g it into electrical

signals, transmitting or storing it and, ﬁnally, reprodu-

cing that speech or music (by means of the opposite

transducer, a loudspeaker). The impor tant point here is

that microphones are designed to be used in a chain, at

which end is, most times, the human ear. Having such

a robust receptor at the end of the chain makes it

unnecessary to be very careful in the design or selection

of a speech input device.

During the last years, however, there has been a

fundamental change in speech communication since

the receiver in the speech communication chain is

not always a human listener any more. Nowadays

machines are used for transcribing speech signals (in

automatic speech recognition) and also, and most im-

portantly in this context, for recognizing the speaker

given a segment of speech (in voice biometrics or auto-

matic speaker recognition). This fundamental change

has brought an uncomfortable reality for all spe ech

researchers: machines are still far less robust than

humans at processing speech.

Of course, the goal of speech researchers is making

machines not even as robust as humans but even more.

Currently, voice biometric systems achieve very good

results in relatively controlled conditions, such as in

telephone conversations with similar durations. This

has been the basic setup for the yearly competitive

Speaker Recognition Evaluations (SRE) organized

by the National Institute of Standards and Technology

(NIST) [2] for the last years. These evaluations

show that currently, technology is capable of achieving

very competitive results in these conditions and is

becoming more and more robust against variabilities.

Voice Device

1377

However, the problem of variability due to the speech

input device is far from being solved. Actually, this is a

very hot research and technological topic. The proof of

it is that next NIST evaluations in voice biometrics will

probably be centered on cross-channel conditions in

which training and testing data come from different

channels (including different microphones, micro-

phone locations (close-talk and far-ﬁeld) and record-

ing devices. However, achieving robustness against

such variations is a long-term research goal that most

probably will not be fulﬁlled in the next few years.

In the meantime, it should be stressed that technol-

ogy is already usable in practical situations, but it should

also be highlighted that current technology may not be

as robust as desirable. In these circumstances it is essen-

tial to take extra care of the design or the selection of the

speech input device. In some cases, of course, the speech

input device is out of control, such as in telephone

applications. But there are other cases where it is neces-

sary to design the speech input device and, in this cases,

it is essential to make the right choice because there are

multiple choices of speech input devices with very dif-

ferent features, and an appropriate selection of the

speech input device could be the key to success or failure

in a voice biometrics application. This section tries to

provide an introduction to the world of speech input

devices or microphones.

Microphones

Definition

A microphone is a transducer that converts sounds (air

pressure variations) into variations of an electrical

magnitude, typically voltage.

History

The early history of the microphone is tied to the

development of the telephone [3]. In fact, the micro-

phone was the last element required for a telephonic

conversation to be developed. One of the earliest

versions of microphones was developed by German

researcher Philipp Reis in 1861. These microphones

were just a platinum piece associated with a membrane

that opened and closed an electric circuit as the sound

made the membrane vibrate. This allowed Reis to build

primitive prototy pes that allowed to transmit voice

and music along several hundred meters. It was several

years later, in 1874, when Alexander Graham Bell pat-

ented the telephone and transmitted what is consid-

ered the ﬁrst telephone conversation ‘‘Mr. Watson,

come here, I want you.’’ Bell improved the microphones

to make them better and better suited for commercial

applications. Among the earlier microphones devel-

oped by Bell there are liquid mi crophones in which a

diaphragm moved a metallic needle in side a metal

recipient ﬁlled with a solution of water and sulfuric

acid, so that the resistance between the needle and the

recipient varied with the movement of the diaphragm.

The latter microphones developed by Bell were based

on the variations of inductance in a moving coil at-

tached to a diaphragm. However, it was not until 1878

that the word microphone was used for the ﬁrst time,

and it was associated with what it is know today as the

carbon microphone. The carbon microphone was

invented by Edison and Hughes, and constituted a

real breakthrough for telephone systems, since they

were more efﬁcient and robust than the earlier devices.

Currently it has mostly been substituted by more mod-

ern microphones that will be described in the following

sections.

Types

All microphones are based on the transduction of air

pressure variations into an electromagnetic magnitude.

However, there are many ways to achieve this, and

therefore there are many types of microphones with

different characteristics and applications. In this article

some of the most important types will be summarized.



Condenser or capacitance microphones. These

microphones are based on the following physical

principle (Fig. 1): the capacitance of a condenser

with two metallic plates depends on the distance

between the two plates. If one metallic plate of a

capacitor is substituted by a metallic membrane

that vibrates with sound, the capacitance of the

condenser varies with sound, and this variation

can be translated into the variation of an electrical

magnitude. There are two ways of doing this trans-

formation. The most common one is trying to set a

constant charge in the two plates and measuring

the variations of the voltage between the two plates.

1378

Voice Device

The other one (slightly more complex) is using the

variations in the capacita nce to modulate the fre-

quency of an osc illator. This generates a frequency

modulated signal that needs to be demodulated,

but the demodulated signal has usually less noise

and can more effectively reproduce low frequency

signals than the one obtained with the constant

charge method. A special type of condenser micro-

phone is the electret microphone. This microphone

is a capacitor microphone in which the charge in

the plates is maintained not by applying an external

constant voltage to the capacitor, but by using a

ferroelectric material that keeps a constant charge,

in a similar way as a magnet generates a constant

magnetic ﬁeld. Condenser microphones are the

most frequently used microphones nowadays, and

it is possible to ﬁnd them from low-quality cheap

versions to high-quality expensive microphones.



Dy namic or induction microphones. These micro-

phones are based on a different physical principle:

when an induction moves inside a magnetic ﬁeld, it

generates a voltage by electromagnetic induction. If a

small coil is attached to a diaphragm that moves with

sounds and if this coil is placed into a magnetic ﬁeld

(generated by a permanent magnet), the movement

of the coil will produce a voltage in its extremes that

is related to the sound. A special type of induction

microphone is ribbon microphones in which the coil

is substituted by a metallic ribbon that vibrates

with sound as is suspended in a constant magnetic

ﬁeld, thus generating a current related to the

sound. These microphones are more sensitive

than coil microphones, but also are more fragile.



Carbon microphones. This microphone is essentially

a recipient ﬁlled with carbon powder and closed by a

metallic membrane on one side and a metallic plate

on the other. As the membrane vibrates with the

sound the powder supports more or less pressure

and its electrical resistance is higher or lower (with

more pressure carbon particles increase their surface

in contact with other particles and this makes elec-

trical resistance decrease). Carbon microphones

were widely used in telephones. Currently they have

been substituted by capacitor microphones.



Piezo-electric microphones. These microphones are

based on yet another physical effect: some solid

materials, called piezo-electric materials, have the

property of producing a voltage when a pressure

is applied to them. Using this property and a piezo-

electric material a microphone can by built by just

placing two electrical contacts on the piezo-electric

material. Piezo-electri c microphones are mainly

used in musical instruments (such as electric gui-

tars) to collect and amplify the sound.



Silicon microphones. Silicon (or chip) microphones

are not based on a new ph ysical effect. Rather, they are

just capacitor microphones built on a silicon chip in

which the membrane is directly attached to the chip.

These microphones can be very small and are usually

associated with electronic circuitry such as a pream-

pliﬁer and a analog-to-digital converter (ADC), so

that a single chip can produce digital audio.

Directional Characteristics

Microphones have different characteristics depending

on the direction of arrival of the sound with respect to

the microphone. A microphone’s directionality pattern

measures its sensitivity to a particular direction.

Voice Device. Figure 1 Principle of functioning of a condenser microphone.

Voice Device

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