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Current and future industrial applications of active noisecontrolColin H HansenActive Noise and Vibration Control GroupSchool of Mechanical EngineeringUniversity of Adelaide, SA 5005Australiachansen@mecheng.adelaide.edu.au

ABSTRACT

The status of active noise control in terms of its application to industrial problems is discussed

and reasons for the apparent lack of enthusiasm for the technology by industry are postulated.

An industrial installation in which the author was involved is used as an example to illustrate the

complexities involved and the reasons why implementation costs are so high. The future of

active noise control in industry is dependent on a number of issues associated with hardware

configuration and cost, user friendly software, generalisation of system design, development of

low-cost, rugged actuators and sensors together with an acceptance of what is possible and what

is not. Novel approaches to achieving the control objective of reduced noise levels at the ears of

industrial employees, which sidestep limitations imposed by the physical properties of sound and

vibration fields, are also required to enable practical application of the technology in many cases.

One such novel approach, which involves virtual sensing combined with very local control and

beam steering that tracks a person’s ear is discussed.

Primary subject classification: 38.2; Secondary subject classification: 37.7

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1. INTRODUCTION

Ten years ago there was widespread belief that in ten years time, industrial applications of

active noise control (ANC) would be quite common. Five years even before that, extensive

advertising and promises were made by at least one company that active noise control would

soon be a feature in most consumer products. In a 1997 paper by the author1, it was pointed out

that the number and range of long term commercial installations was relatively small and that

perhaps the main reason for this was the excessive cost of system installation which could easily

exceed the cost of the basic control hardware by a large amount. The need for tuning of systems

by experts in the technology also adds a significant cost component as does the unique nature

of most problems which makes the use of a generic system difficult.

It was pointed out in the 1997 paper that what was needed was an “inexpensive, clever,

commercial control system which includes a selection of source and sensor transducers to satisfy

most problems, and software to guide users in the correct choice and location of such

transducers”. Details of the system requirements are also discussed in the 1997 paper. Such a

generic, user-friendly, easily-operated system is an essential pre-requisite if industrial

applications of the technology are to increase. In the years since 1997, no such system has been

developed, although work on active control continues in laboratories in a number of places

around the world.

Although the lack of a suitable generic controller has hindered industrial application of ANC

technology, another contributor has been the bad publicity resulting from claims made by some

companies in the past, which were never realised. As in many emerging fields of technology,

unscrupulous patenting of ideas which have been published by other unrelated researchers in

journal papers or consulting reports has also hindered commercialisation efforts by small

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companies who cannot afford to become embroiled in court battles proving the invalidity of

relevant patents.

This paper focusses on industrial applications of ANC technology and as such, military and

aerospace applications are not discussed, even though there are a number of successful examples

of ANC in these areas. However, these applications are not affected by the same cost constraints

that are a reality in industry and so the extension of the technology to general industry is rarely,

if ever, feasible.

In the remainder of the paper, some currently practical industrial applications of ANC will

be mentioned. This will be followed by an example of an industrial installation managed by the

author, which will be used to illustrate some of the issues involved and problems that need to be

overcome. Next, the types of commercially available control systems currently available will

be described and this will be followed by a description of current hardware and software

development being undertaken by the author and his colleagues. Finally, novel approaches to

reducing noise levels around the heads of people working in noise environments will be

discussed. This includes virtual sensing, head tracking, control source localisation and steering.

2. CURRENT INDUSTRIAL APPLICATIONS OF ANC

Current industrial applications of active noise control technology are limited mainly to the

control of plane wave sound propagation in air handling ducts, gas turbine exhausts or diesel

engine exhausts, and these are mainly feedforward systems. Active ear muffs represent a

successful application and these are usually feedback systems. Considerable development effort

has been spent on these and they have been successfully used in environments in aircraft and in

some industries. Because active ear muffs use feedback control principles (as this requires no

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reference signal), they perform best when the error signal auto-correlation coefficient is

relatively high. They also suffer from problems in high level noise environments due to the

limited output capability of the small loudspeakers used. In impulsive noise environments use

of a feedback control system can result in ringing because of the limited frequency response of

the control system. Active ear muffs are relatively well known and will not be discussed further

here.

Another application that has been successfully commercialised is the reduction of tonal noise

in propeller driven aircraft using active engine mounts and vibration actuators mounted on the

fuselage rings2. This technology is more applicable to the aerospace industry rather than general

industry and also will not be discussed further here.

The most successful industrial applications of active noise control target single or multiple

tonal noise, but applications also exist that produce significant noise reductions (10 to 15 dB)

over a 1-1/2 to 2 octave frequency range. It is difficult to achieve a greater bandwidth of active

control with existing commercially available hardware and software. However, it is possible to

extend the control bandwidth by using a filtering system coupled with multi-rate sampling. This

has yet to be implemented in any commercial controller and will be discussed in Section 4A.

When implementing a controller in an industrial environment, there are a number of issues

that must be considered, even though most of them can be completely ignored for a laboratory

demonstration. There is a huge step from demonstrating ANC in a laboratory to implementing

a robust, reliable system in an industrial environment. The requirements that must be met for a

successful industrial installation include:

• a robust reference sensor;

• a noise free reference signal that is well correlated with the error signals;

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• robust control sources well protected from the environment;

• robust and reliable error sensors well protected from the environment;

• a reliable control system capable of self starting from a power shut down by throwing a

single master switch with a time delay between switching amplifiers on and switching on the

rest of the system;

• a system that is either immune to transient events or can shut itself down on detection of such

an event and restart after such an event passes;

• remote monitoring capability either by internet or modem;

• automatic readjustment of the controller should an error sensor or control source fail;

• automatic shutdown in the event of sufficient sensor or actuator failures to make the system

ineffective;

• alarms to indicate which error sensor channel or control source channel is defective; and

• fail-safe mechanisms to prevent noise levels ever exceeding the existing primary noise levels

prior to control for any reason such as controller instability caused by transients or transducer

failure.

It is important to remember that until now and probably for the foreseeable future, active control

systems are an extremely complex form of noise control technology compared to existing passive

alternatives. This complexity has in the past, translated into unreliability and excessive cost so

that now the situation exists where active noise control solutions in industry are only considered

where alternative passive solutions are either impractical to implement or exorbitantly expensive.

The only means by which this trend will be reversed is by gradual demonstration of relatively

low cost, easily maintained, reliable systems and this situation is still some way into the future.

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3. AN EXAMPLE OF THE DEVELOPMENT AND INSTALLATION OF AN

INDUSTRIAL ANC SYSTEM

Perhaps the requirements for successful ANC systems are best illustrated by an example of

an actual installation that includes many of the capabilities listed in the previous section. The

example is a system used to control sound propagation in the exhaust stack of a spray dryer unit

used in a dairy factory. Perhaps the most complicating aspects of this example were:

• the frequency at which control was desired was above the higher order mode cut on

frequency; and

• the sound field in the duct was never steady so that the error sensor outputs varied rapidly

and continually in amplitude and phase.

The aim of the ANC system to be described here was to reduce the amplitude of tonal noise

at the fan blade pass frequency, emanating from the top of the spray dryer exhaust stack and

radiating into the surrounding community. The diameter of the exhaust stack is 1.6 m, the

temperature range of exhaust air is from 60 to 90 degrees C and the blade pass frequency ranges

from 170 to 190 Hz. Calculations show that for this stack, two higher order modes propagate,

thus greatly complicating the control system required. Not only do higher order modes exist, they

exhibit “spinning” characteristics which result in the nodal lines twisting with respect to a

reference, with the twist angle being a function of axial location in the duct. The twist angle at

a fixed axial location was very sensitive to temperature fluctuations and this resulted in quite

rapid and large sound pressure fluctuations at any given location in the duct. This is discussed

a little more in the controller section to follow. Of course, a conservative approach would have

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been to weld axial splitter plates in the duct so that only plane waves could propagate between

the control source and error sensors. For these to be effective, the welds would have to have been

air tight. As the duct was stainless steel and vertical, it was considered too difficult to implement

this option, which is why a multi-mode control system was developed.

One of the expensive and time consuming aspects of the installation was the signal cabling.

Extensive lengths of signal cabling were needed to connect the reference and error sensors to the

controller and to connect the controller to the loudspeakers, all of which were physically

separated by many tens of metres. Care had to be taken in joining wires to minimise the

possibility of corrosion of the joints and the introduction of extraneous electrical noise. In all

cases, water proof junction boxes were used. Due to the sensitive nature of the DSP chips and

other electronic components, the controller had to be located in a temperature controlled room

and space was found along side other process control instrumentation.

Interesting practical aspects associated with each element of the control system are described

in the following sections. It is assumed that readers are familiar with the general configuration

of a multi-channel feedforward active noise control system, and if not appropriate textbooks may

be consulted 3. The main components and their associated technical challenges, which will be

discussed here, are the reference sensor, the error sensors, the control sound sources and the

controller.

A. Reference Sensor

The reference sensor is used to derive a signal that is used by the controller to generate the

driving signal for the control loudspeakers. For the spray dryers, the reference signal was derived

using a Hall effect tachometer which produced an electronic pulse each time a gear tooth passed

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its field of view (see Figure ?). The gear wheel was manufactured especially for the project and

was mounted on the fan drive shaft. The reference signal was generated digitally by passing the

tachometer signal into an Analog Devices Sharc EZ-kit 21061 DSP board. The time between

successive tachometer pulses was measured by passing the signal into the digital interrupt port

which interrupts the central processor and allows it to count clock cycles between successive

pulses. As the CPU runs at 40 MHz, the resolution is 25 nano-seconds. The DSP is programmed

to generate a sine wave with the same period (or any required multiple thereof) and output it with

a sample rate of 16 kHz through a low pass filter into the control system reference signal input.

The reference signal sine wave thus remains synchronised with the fan blade passing frequency

at all times.

B. Error Sensors

The large turbulent pressure fluctuations in the duct propagate at the speed of the air flow and

not the speed of sound and thus their presence complicates the error sensor design which ideally

should only respond to acoustic pressure fluctuations if undesirable control signals are to be

avoided. Initially, microphones were placed in the end of a 2m long porous tube with the

intention of amplifying the acoustic pressure fluctuations and attenuating the turbulent pressure

fluctuations. This approach had two main problems. The turbulent pressure fluctuation rejection

was insufficient and anchoring the tubes in the vertical duct as well as protecting them during

cleaning operations was problematical. They also represented a possible site for bacterial growth.

The final design was a flush wall-mounted sensor, consisting of an electret microphone inserted

in a tube with its own preamplifier, connected to an enclosure filled with a block of acoustic

foam protected by a metallic foil to filter out some of the turbulence-related pressure fluctuations

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(see Figure 2). Small disks of Vyon, a porous plastic, were inserted in the tube in front of the

microphone to attenuate the sound level incident on the microphones so as to avoid mechanical

saturation of the microphone membrane, because of the high acoustic levels present inside the

duct. As shown in the figure, a continuous air flow is maintained over the microphones to keep

them cool. The aluminum foil facing on the acoustic foam insert prevents contamination of the

microphone system by milk powder.

With this design, background pseudo sound was reduced to more than 13 dB below the

targeted acoustic error signal. These sensors have been operating successfully for over a year

with zero failure rate.

Twelve error sensors were installed on the exhaust stack, flush with the duct wall. They

were organised in three rings of 4, 5 and 3 sensors respectively, as shown in Figure 3. The

three rings are respectively at a distance of 0.20 m (No. 6,7,8,9), 0.70 m (No. 1,2,3,4,5), and

1.85m (No. 10,11,12) below the exhaust duct outlet plane.

C. Sound Sources

Due to the high temperature and sometimes wet environment, it was necessary to provide

protection for the loudspeaker cones and cooling for the coil. At first, a membrane of Viton, a

rubber-like material, was tried. Even though this was limp and relatively lightweight with a

transmission loss close to zero at 200 Hz, it had a dramatic effect on the loudspeaker output,

reducing the maximum achievable sound level by 20 dB, probably as a result of the viton

material acoustically loading the loudspeaker and changing its radiation impedance. As the

loudspeakers have to generate sound levels in the duct of up to 138 dB, this amount of loss was

unacceptable. The final design consists of a 600W paper cone, low-frequency loudspeaker,

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coated with a silicon compound to protect it against humidity, and then a reflective coating to

protect it against dust particles and heat build-up. To further discourage the build up of milk

powder dust on the speaker diaphragm, it would be useful to introduce a small air jet close to

where build-up is possible. The enclosure at the back of the speaker is connected to the front face

of the speaker with a small tube to ensure pressure equalisation of the front and back surfaces

of the speaker cone. Without this equalisation, the speaker cone would suffer a DC shift and fail

to function correctly. The heat generated by the speaker coils in operation is dissipated using

pressurised air injected in the vented speaker backing enclosures. The speakers have survived

so far for over a year in their hot and humid environment, with failures that have occurred being

due to over pressurisation of the air cooling system, resulting in bursting of the paper cones.

Each speaker enclosure is mounted flush with the duct wall and one to two speakers are

mounted at each of four different axial locations, covering a total of seven different locations,

as shown in Figures 4 and 5. The top speakers are mounted approximately 3500 mm below the

duct exit plane. During operation, the 600W speakers are driven at about 5W, which minimises

harmonic distortion and maximises speaker life expectancy.

D. Controller

A feedforward controller using a periodic block FXLMS algorithm4, (Eqs. 12-14), with control

filter updates carried out every 15th sample) was chosen for the task. The periodic block FXLMS

algorithm is less computationally intensive than the standard FXLMS algorithm as it allows

control filter updates to be done after a block of error sensor data has been collected. There are12

input channels for the error signals and 6 output channels for the control signals. The reference

signal is derived from the tachometer on the fan shaft so there is no acoustic feedback from the

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control sources to the reference sensor. The cost function is the average sound pressure from all

12 microphones and even though the sound pressure recorded by individual microphones

changes quite quickly, the average changes much more slowly and this allows the control system

to converge to an optimum. Note that the cost function also includes a quantity proportional to

the control signal outputs, which is equivalent to including leakage3. Other cost functions such

as weighting the microphone signals differently so that more effort would be directed towards

controlling the larger signals, were tried but not found to be any more effective than the average

mentioned above. The optimum size of the convergence coefficient was found by trial and error

as a compromise between convergence speed and instability risk, and was adjusted on site during

system set up. Needless to say the acceptable stability risk was zero so a relatively small

convergence coefficient was used.

It is well known3 that to ensure stability of the control system, it is necessary to have an

accurate estimate of the cancellation path impulse response or transfer function (CPTF). In many

laboratory experiments, this is done off-line prior to starting up the controller, using random or

pseudo random noise injected into each control source in turn. For any industrial application, this

is the preferred way to start up the system. However, once the system begins to operate, it is

generally not possible to shut it down to check the CPTF and an on-line procedure is needed.

Various schemes were tried on the spray dryer system. The use of the primary noise signal to

determine the CPTF as described by Sommerfeldt and Tichy5 is only accurate for single channel

systems. Another scheme that was considered for the spray dryer system involved the use of an

introduced tone, several decibels lower in level than the blade passing tone and at a range of

frequencies around but not too close to the fan blade passing frequency. When the blade passing

frequency varied, the frequencies missed previously would be tested, resulting in the gradual

updating of a table that could be stored in memory. Each time the fan frequency changed, the

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table could be looked up for the most recent CPTF data. However, if the fan ran for any length

of time at the one speed and other environmental conditions changed, this method may not take

into account changes in the CPTF and the system could go unstable. Thus this scheme was

rejected as too risky and it was eventually decided to use low level random noise, 30 dB below

the level of the blade passing tone, introduced into each control speaker in sequence. Using such

low level noise, which was not detectable by ear, required very long averaging times to

determine the CPTFs but that has not proven to be a problem.

E. Controller Hardware

The controller hardware used for the project is shown schematically in Figure 6. The

EMERALD modules are DSP boards based on the Analog Devices ADSP21062 processor and

they are suitable for executing both system-level software (such as the interface to the PC) and

signal processing software, such as system modeling and control filter adaptation. In the current

system, one EMERALD board is used for each purpose, with one acting as a slave to the other,

which is the master. The interface between the modules is implemented using the processor's

high speed link ports.

Not shown in the above layout is the Analog Devices EZ-Kit 21061 which was used to

process the reference signal input pulse train to produce a sine wave at the blade passing

frequency, which was used as the reference signal input to the system in the above figure. The

controller front panel layout is illustrated in Figure 7.

Some of the problems that were overcome during commissioning included:

• excessive noise on the tachometer signal due to electricians laying signal cables next to

power cables;

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• electromagnetic pickup in DSP board communication lines due to radiation from motor

speed controllers and other process equipment;

• power failures resulting in a requirement for operator intervention during restart;

Novel features included:

• automatic identification and alarm set state for any failed speaker or microphone

channels;

• system shutdown and temporary rest if noise levels exceeded primary noise levels;

• system reset if algorithm instability detected;

• continuous low-level system identification;

• ability to self start after a power failure.

F. Control System Performance

The performance of the system was measured in the community rather than the error sensors

in the duct (where the performance was much better). Noise reductions in the community ranged

from 9 to 14 dB at the blade passing frequency with the reduction being such that the tone was

no longer noticeable. A typical result is shown in Figure 8.

4. CURRENT RESEARCH DIRECTIONS FOR INDUSTRIAL APPLICATION OF ANC

To make active noise control more accepted in industry, considerable effort is needed to

conquer the last remaining barriers to this acceptance. It is true that the limitations of the

technology are better understood than they were ten years ago. Now the thrust is rightly directed

at using innovative means to overcome these apparent limitations, some of which are a result of

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the laws of physics and some of which are a result of insufficient power and intelligence in

current commercial control systems, or the lack of sufficiently robust sound and vibration

generating transducers. Industrial applications of ANC are special in that any installed system

must be electrically as well as mechanically robust and should function for years with no

maintenance. Thus relevant research is or ideally should be directed towards resolving a number

of issues, which include the following:

• Development of low cost, yet rugged, high output inertial actuators for vibration isolation

and structural vibration (and noise radiation) suppression. This is important in cases where

vibrating equipment is exciting structures which radiate unwanted noise.

• Development of low cost, yet rugged, high output sound sources for sound suppression.

Although this development is important if active noise control is to become more widely

accepted in industry, there is very little relevant work reported in the literature. The

generation of high sound levels in ducts and muffler systems may be achieved using horns

that are designed to couple with a duct rather that free space. Although no work has been

reported using this approach, some work has been reported recently on the use of a flapper

valve to generate the high level cancelling signal in muffler systems6,7.

• Development of control source systems that are remote from the location where noise

reduction is required. These may take the form of non-planar speaker arrays to direct

cancelling energy into desired areas with minimal effects in other areas.

• Development of remote virtual sound pressure and energy density sensing systems so that

microphones do not need to be located near where the sound field is being cancelled or

suppressed.

• Development of tracking systems that can keep track of people’s head locations and direct

the cancellation energy and virtual sensing location to where it is required.

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• Development of a third generation electronic controller with sufficient power to cope with

the new control source and sensing systems and sufficiently user friendly to be useful with

minimal training.

• Development of various control algorithms tailored for specific problems, some of which are

listed below. A number of appropriate control algorithms are discussed by Qiu and Hansen4.

– Tonal noise rapidly varying in amplitude.

– Tonal noise rapidly varying in frequency.

– Broadband noise covering 1, 2 or 3 octaves.

– Combination of tonal and broadband noise.

• Development of system management software necessary for effective, automatic and user

friendly system operation in an industrial setting.

• Transient suppressors on controller inputs and outputs to prevent overloading of control

sources and undesirable source outputs in the case of a transient event being recorded by an

error sensor.

Work at the University of Adelaide is currently focussed on three of the above aspects that

are driving the technology forward towards the goal of widespread industrial acceptance of the

technology. The first aspect is concerned with the development, currently underway, of a third

generation controller which, when complete, will be somewhere between existing controllers and

the required future intelligent controllers in terms of capability. The second aspect is concerned

with overcoming physical limitations associated with the small zone of silence that exists around

error sensors in enclosed sound fields. The third aspect is associated with developing a focussed

and steerable cancelling sound field.

A. Control System Hardware Development

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With continual advances in DSP memory capability and speed, and steady reductions in cost

per unit of capability, it is becoming more and more practical to develop multi-channel

controllers capable of having thousands of taps in the control filters and cancellation path

modelling filters. Note that the number of cancellation path modelling filters needed is equal to

the number of control channels multiplied by the number of error channels.

One of the big problems with the development of an electronic active noise control system

is the difficulty in obtaining funding for it. It is too risky in terms of potential economic returns

to attract industry support and too applied to attract support from research granting bodies.

Perhaps the best hope lies with Defence Departments which can see a use for such technology

for solving particular problems.

B. Third Generation Controller Architecture

In deciding on the appropriate architecture for a third generation controller, one must choose

from a number of possible fundamental options. Briefly, there are three practical fundamental

options, which are:

1. Use currently available DSP boards and IO boards which can be inserted in a PC and

programmed in “C” and run via the Windows operating system. Thus if more powerful

boards become available, they can simply replace existing boards with negligible, if any,

impact on the software requirements. Unfortunately, the Windows operating system

suffers from a large overhead in terms of memory and a system programmed in this way

will invariably suffer due to processing speed limitations. This type of system is also too

expensive and insufficiently robust for industrial applications.

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2. Put a general-purpose DSP board in a custom enclosure with power supply and custom

I/O boards, programmed using DSP programming tools. Unfortunately the continued

supply of these boards is often problematic, their specifications are unreliable and they

have many components that are not needed for active noise control. There are also

considerable problems interfacing General purpose DSP boards with I/O boards from

another manufacturer.

3. Construct a multi-processor DSP board from scratch with sufficient power and memory

to meet the most demanding ANC application. Include it in a modular system which

allows the use of multiple DSP boards and multiple IO modules which can be tailored

to the number of channels needed and the processing power and memory required.

The third option offers more flexibility in terms of producing a system optimised for active

noise and vibration control. Such a system is currently being developed with the intention that

it will have all of the properties listed as dot points in Section 2. It will also have the capacity for

multi-rate filtering (as discussed in Section 4A) with the associated extension of good

performance from two to five octave bands. The intended future system configuration is

illustrated in Figure 9.

The multi-rate filtering will be achieved in the I/O modules (labelled as “Analog I/O” in the

figure), as each module will contain a high speed A/D converter, a D/A converter and a low cost

DSP, all sampling at 250 kHz. The DSP on each I/O board will manage the required down-

sampling and will allow multi-rate filtering as described in the next section. Note that there will

be one DSP for each channel, which will provide ample processing power for all the I/O

management tasks and multi-rate filtering as well as transducer failure and signal overload

management.

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The central processor board (labelled “DSP” in the figure) will contain 4 Analog Devices

DSP Sharc (floating point) processors. Using more DSPs on the one board does not significantly

improve the overall speed of the system due to bottlenecks associated with using external

SDRAM (necessary for the thousands of filter taps envisaged).

The controller will also be capable of being monitored, diagnosed, reset and set up remotely

through the internet (with appropriate security of course).

The new controller will allow a choice of a number of different control algorithms, including

frequency domain algorithms optimised for both tonal and broadband sound field control.

Feedforward, feedback and hybrid algorithms will be available and advanced users will also be

able to program their own control algorithms. Cancellation path identification will be possible

both on-line and off-line.

The hardware will be configured to minimise digital delays and to provide a large capacity

for filter taps for both control filters and cancellation path estimates.

C. Bandwidth Extension Using Multi-Rate Filtering

One of the limitations of current active control systems is the limited bandwidth over which

they operate (usually 1-1/2 to 2 octaves at most). However, it is possible to extend the control

bandwidth by using a filtering system coupled with multi-rate sampling, but as yet this has not

been implemented in a commercially available controller. What this arrangement means in

practice is that the input signals from the reference and error sensors are sampled at a very high

rate (250 kHz) and filtered into octave bands using a number of digital filters. The high sample

rate eliminates the need for anti-aliasing filters. Each octave band signal emerging from a digital

filter is then re-sampled at the optimum sampling rate for that particular frequency range (usually

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about 15 times the centre frequency of the octave band). For each octave band filter, a control

filter with adjustable weights is needed to provide the control signal and the control filter outputs

for each octave band need to be re-combined prior to being converted to an analog signal and

sent to the amplifier driving the control actuator. For a multi-channel control system, one set of

filters is needed for each channel.

A five-in, four-out multi-rate sampling system is illustrated in Figure 10, where the

arrangement is shown for a single octave band and the combination with the systems for other

octave bands is done at the output labelled as “sum over all octave bands”. Following the octave

band boxes on the inputs, a digital down-sampler adjusts the sample rate so that it is optimum

for the particular octave band. It can be seen that the processing power needed to implement such

a system is enormous and current technology is only just up to the task, even though the idea was

first floated many years ago8.

Another limitation with current control systems is the need for analog anti-aliasing filters

which must be duplicated for each sampling rate selected. One way around this is to use 1-bit

Codec A/D and D/A converters which sample at a very high rate (1 MHz or more) and through

some interesting hardware manage to produce a 16 bit result at 32 kHz, with the capability of a

final rate as low as 5 kHz. The most unfortunate property of codecs is that they have a 30 sample

delay at the final sample rate. At the 32 kHz rate this is 1 millli second and at the 5 kHz rate it

is 6 milli-seconds which is a serious problem in many cases where it is necessary to control

random noise. It is not a problem for control of periodic noise, nor for the control of random

noise in a duct where it is possible for the control source to be further than 2.1 metres from the

reference sensor. Delays introduced by loudspeaker control sources increase this required

distance in practice, of course. One way of achieving low sample rates for the control of very low

frequency disturbances is to use digital down sampling based on the highest sample rate possible.

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In this way the delay is the 30 samples at the high rate plus whatever overheads are used in the

down sampling. Some results of doing this with one particular commercial controller are shown

in Figure ?, where it can be seen that the reduction in time delay is slightly less than the down-

sampling divider factor due to the computational overheads associated with the down-sampling.

It can also be seen that the time delay is slightly dependent on the frequency of the input signal

(shown as different symbols for different input data frequencies).

D. Multiple Independent Single Channel vs Multi-channel Fully Coupled Control

There is currently considerable speculation supported by anecdotal evidence that if a

sufficiently large number of control sources are used in a feedforward active noise control

system, it is not too important where they are placed in terms of achieving a global reduction in

some cost function. That is, it is not necessary to go through an optimisation process to optimally

place the control sources and error sensors. In an attempt to further simplify the control process,

Fuller and Carneal9 suggested using hierarchical bio control in which a small number of signals

are sent from an advanced, centralized controller and are then distributed by local simple rules

to multiple control actuators. No commercial systems currently use this approach, even though

it was suggested more than ten years ago.

Some recent results comparing the effectiveness of multiple single channel control systems

versus a single multi-channel control system attached to the same sensors and actuators are

shown in Figure 1210. In the figure are shown results for vibration control of a cantilevered beam

using a foam damper, inertial actuators (unactivated) in the foam, activated inertial actuators

driven by single channel controllers and activated inertial actuators driven by a multi-channel

controller. It can be seen that for this simple example involving broadband control, the multi-

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channel controller achieves much better results. Thus there is considerable impetus for the

development of a robust, multi-channel modular controller that can be easily expanded if

necessary and has all of the features listed as dot points in Section 2.

Nevertheless the multiple single channel controller case also reflects substantial noise

reduction, especially at resonance frequencies. As multi-channel controllers have problems such

as convergence speed, there is an increasing trend towards the use of multiple single channel

controllers for cases where their performance is acceptable and the improved noise reduction

possible with a multi-channel controller is either not needed or not possible due to tracking speed

requirements for noise environments that are barely quasi-stationary. As more is understood

about the performance of multiple single channel controllers, they may find application in

industrial situations as a result of their simplicity, low cost, tracking ability and adaptation speed.

E. Reference Signal Generation

As use of feedback control to actively reduce noise levels is impractical outside of ducts and

headsets, it is important to be able to measure an appropriate reference signal so feedforward

control can be implemented. It is also impractical to have long runs of cabling from reference

sensors to the active control system, so it will be necessary to develop a system that transmits

the reference signal to the control system without the need for wiring or cabling. Two types of

reference signal may be envisaged: one derived using a microphone close to the noise source for

random noise and one derived from a tachometer signal for periodic noise synchronised to a

rotating shaft.

One application of great interest for which the generation of an adequate reference signal is

difficult is for the control of random noise propagating in a duct containing a relatively high

22

speed air flow. The problem with the presence of the air flow is that it produces turbulent

pressure fluctuations which are not related to the noise to be controlled, as they only propagate

at the speed of the flow and not the speed of sound. Unfortunately, a microphone is incapable

of distinguishing between turbulent pressure and acoustic pressure fluctuations and the presence

of the turbulent pressure fluctuations reduces the correlation between the reference and error

microphone signals, which in turn reduces the capability of the active noise control system to

achieve a reduction in noise. There are a number of approaches that have been used in the past

to try to separate the turbulent pressure fluctuations from the acoustic pressure fluctuations prior

to them being sensed by the microphone. One method commonly used is to place the microphone

at the end of a long porous tube, which results in the acoustic pressure fluctuations being

reinforced because they are travelling at similar speeds inside and outside of the porous tube.

Perhaps a more clever way of achieving the same result is to use a number of reference

microphones distributed around the duct wall at the same axial location and then adding all of

their signals to produce a single reference signal. This has the effect of cancelling the random

turbulent pressure fluctuations (as their phase will be random at all microphone locations) while

at the same time enhancing the acoustic response, as the plane acoustic wave will be in phase at

all microphone locations. Of course, this also has the effect of excluding higher order modes

should any be present. In addition, any phase mismatch between microphones will limit the

effectiveness of this technique.

When periodic noise is to be controlled and the noise is synchronous with a rotating shaft,

it is common to use the output of a tachometer as a reference signal. Unfortunately this output

is usually a pulse that occurs once per revolution and what the active control system ideally

needs is a periodic reference signal that contains the frequencies to be controlled and the

reference signal amplitude at these frequencies should be directly proportional to the required

23

noise reduction at each corresponding frequency. Thus there are various techniques to convert

the once per revolution pulse from a tachometer into the required multi-sinusoid reference signal.

The first step that is often taken is to attach a gear wheel to the rotating shaft so that the

tachometer (magnetic or optical pickup) puts out a pulse each time a gear tooth passes it and the

number of pulses per second correspond to the fundamental frequency to be controlled.

However, this still results in many unwanted higher order harmonics in the reference signal (due

to the non-sinusoidal shape of the tacho signal) and further signal processing is needed to

eliminate the unwanted harmonics and generate the wanted harmonics with the optimum relative

amplitude to the fundamental. This may be done using a DSP board as described in Section 3A.

F. Cancellation Path ID

It is necessary to implement a procedure to update the cancellation path transfer function

estimates at regular intervals as any given estimate becomes less valid over time. Such a

procedure involves switching off the active noise control system filter weight update process and

then introducing low level random noise for a short time (a second or less) to re-estimate the

cancellation path impulse responses. Then the control filter weight update can be re-started. This

process would be repeated automatically whenever a significant reduction in control performance

was detected. A smarter system can be further developed as follows: whenever the total error is

out of the normal range, the controller can measure a new cancellation path model and then save

the transfer functions with other relevant parameters such as a fan speed (if in a duct) and

temperature. The next time a similar situation is encountered, the controller can automatically

recall the saved cancellation path transfer functions and use them for the new controller filter

24

weights update. In this way the controller gradually becomes more intelligent and more adapted

to its environment.

5. OVERCOMING LIMITATIONS IMPOSED BY PHYSICS

Most industrial sources are located in enclosed spaces and in many cases there are a number

of sources contributing to the noise problem in any one location. Global control in enclosed

spaces and in free space is difficult to achieve, even for tonal noise11 and local control is

unsatisfactory as it invariably includes a volume which is too small. One approach that has been

used in the past is to incorporate an ANC system in a headset. While this approach has had some

success in aircraft cabins, success in industry has been limited for two reasons; they are not

particularly effective in high noise level environments or in environments where the noise is

impulsive or rapidly changing, and they are not very comfortable to wear.

Work currently being undertaken is directed at overcoming both of these limitations. In

addition, work is underway to develop a system that will have the same effect as ear muffs

without any hardware on the ear of the exposed person. This approach involves developing a

steerable, focussed anti-noise signal at a person’s ear, without the need for sensors or active

noise control sources near the ear, and in such a way that the increase in noise levels at other

locations is relatively small. Such a system can be divided into the following parts, all of which

require some shift away from standard ANC technology and all of which are discussed in more

detail in the following sections.

• Virtual sensing, whereby the physical error sensors are remote from the ears, and

incorporating energy density sensing for more gradual sound pressure gradients near the

minimum sound pressure level location;

25

• radio transmission of reference signals from tachometers or microphones close to noisy

equipment;

• focussing of the active noise cancellation field, generated by using planar and non-planar

arrays of small, low cost speakers or by using the audio spotlight;

• tracking the ear locations of the noise exposed person;

• steering the sensor system and the active noise cancellation field generator to ensure that the

location of minimum sound pressure moves with the ear; and

• developing the third generation electronic control system (described in the previous section)

capable of handling the immense computational load and capable of remote monitoring, reset

and set up.

A. Energy Density Sensing

It has been shown by a number of authors11-13 that it is not feasible to attain global sound

reduction using active control of large enclosed sound fields. Thus one is left with the possibility

of achieving small, localised regions of reduced sound pressure level. In conventional active

noise control systems, these localised regions are at and close to the error sensors, which clearly

is not practical as the regions of minimum noise level should be at the ears of people who need

to be protected. Another problem often encountered is the large pressure gradient in the vicinity

of the error microphones which is characterised by a large sound pressure reduction at each error

microphone, and a rapidly increasing sound pressure level as the distance from the microphone

increases. The size of the zone within which at least 10 dB of sound reduction can be achieved

is about 1/10 of a wavelength of sound, but the worst problem is the severe pressure gradient

26

within that zone which results in sharp changes of sound pressure level as one moves small

distances away from the error microphone.

One promising means of reducing the severity of the pressure gradient in the vicinity of the

microphone is to use energy density rather than just pressure as the cost function to minimise14.

An energy density transducer senses pressure gradient (or three dimensional acoustic particle

velocity) as well as acoustic pressure. A first generation energy density sensor, containing 4

microphones is illustrated in Figure 13. However, this design, which relies on the use of low-cost

electret microphones is effective only over a limited frequency range: the low frequency limit

defined by the lack of phase matching between the microphones and the high frequency limit

defined by the microphone spacing no longer allowing an accurate estimate of the pressure

gradient from the pressure difference between adjacent microphones. Recently, a new type of

energy density sensor has been developed by Phone-Or Ltd., an Israeli company. This three

dimensional energy density sensor consists of three optical pressure gradient microphones and

a single optical pressure microphone. For each pressure gradient microphone, the pressure

gradient is determined from a measurement of the velocity of a membrane mounted in a small

closed tube, with one small hole in the tube wall on each side of the membrane. A LED in the

control unit sends light through a fibre optic cable to the membrane, the light reflected back from

the membrane is collected using a photo-detector and the doppler shift in frequency determined

to give a measure of the membrane velocity and hence the pressure difference across it. The

pressure microphone operates in a similar manner, except that one of the holes in the closed tube

is very much smaller than the other, so that for incident pressure signals at frequencies above

about 10 Hz, it appears effectively closed. The optical energy density sensor is illustrated in

Figure 14, where it can be seen that it is a much more compact transducer than that illustrated

27

in Figure 13. It has a dynamic range of 80 dB and covers a frequency range from 10 Hz to 4 kHz

with less than 1% harmonic distortion at 94 dB SPL and less than 5% THD at 120 dB (1 kHz).

Energy density sensing results in smaller noise reductions at the error sensors but a much

smaller pressure gradient in the sound field near the error sensor. It also results in an extension

of the 10 dB reduction zone to one half of a wavelength.

B. Virtual Sensing

The use of pressure measurements at one or more locations to estimate the sound pressure

at another “virtual” location is known as virtual sensing and there are several schemes that have

been used in the past for determining the estimated pressure at the virtual location. The first

scheme15, 16, illustrated in Figure 15, involves placing a microphone at the virtual location and

measuring the transfer function between that microphone and the physical microphone that will

be used for the future pressure estimates at the virtual location. The transfer function is then used

in the control algorithm to drive the control source signal necessary to produce a minimum

pressure at the virtual location. A disadvantage of this approach is that the primary sound field

contribution is assumed to be the same at the actual microphone location and the virtual location.

This is approximately true when the distance between the primary source and actual microphone

location is large compared to the distance between the actual and virtual microphone locations

and the latter locations are in the far field of the primary source. One advantage of this approach

is that it is robust in terms of primary source location. That is, it will give similar results

regardless of where the primary noise is coming from. More recently Popovich17, Roure &

Albarrazin18 and Friot et al.19 modified the previous approach slightly to take into account the

difference in primary sound pressures at the virtual and actual microphone locations and labelled

28

it the remote microphone approach. The method is essentially illustrated in Figure 16.

Unfortunately, none of the previous approaches are easily amenable to being able to compensate

for head movement, which effectively requires continual changing of the virtual location.

An alternative way of estimating the pressure at the virtual location20-23 is to use linear or

quadratic extrapolation of the signal measured at a number of physical microphones (2 to 4 for

a 1-D sensor and 6-9 for a 3-D sensor). This approach is shown in Figure 17 for a 1-D system,

which illustrates both linear and quadratic extrapolation. Such a system can be used to track head

movement as the head location, xv is a variable easily entered into the governing equations as

shown in Figure 17.

Perhaps the most successful approach used so far is that suggested by Cazzolato24 which is

a slight variation of the remote microphone technique suggested by Roure and Albarrazin18 and

very similar to the technique for harmonic noise reported by Gawron ans Schaaf25 in an obscure

conference paper in 1992. This technique involves using an adaptive LMS algorithm to adjust

the weights applied to a number of microphone signals, which when combined, provide an

accurate estimate of the sound pressure at the virtual location (see Figure 18).

Some promising work has also been completed on 1-D virtual energy density sensing20. Work

is continuing on the development of 3-D pressure and energy density sensors using the adaptive

scheme described above to estimate the sound pressure at the virtual location. One interesting

discovery is that when a sufficient number of sensors completely surround the virtual location,

excellent results can be obtained in terms of the estimated sound pressure at the virtual location.

At the time of writing, it is not clear whether multiple pressure sensors or multiple energy

density sensors will provide the best results. Preliminary experiments with a mining equipment

cabin26 showed that for broadband excitation it was difficult to distinguish between the overall

performance obtained using pressure sensing as compared to energy density sensing. However,

29

energy density sensing resulted in a smoother controlled sound pressure spectrum for which the

peaks and troughs in the spectrum were smaller than those obtained with pressure sensing.

Other work currently in progress involves the use of structural vibration sensors to estimate

the sound pressure at a particular virtual location when the source of the noise is a vibrating

structure.

C. Focussing and Steering the Cancelling Sound Field - Speaker Arrays

It is relatively well known that planar speaker arrays can be used to generate, focus and steer

sound fields. For active control of sound in enclosures, they allow for the possibility of local

sound cancellation near a person’s head and the possibility of steering the cancelling sound to

ensure it continues to envelop the head. The required size of the array for effective operation is

about 2 wavelengths square, which equates to about 3.4 metres at 200 Hz. Clearly this may not

be practical in small enclosures such as mining vehicle cabins. However, if the speakers are

placed in the ceiling and walls of the cabin in a non-planar array, it will still be possible to focus

and steer the cancelling sound with the advantage that a smaller array size will be effective (see

figure 19). To minimise the effects of side lobes it is necessary to use “amplitude shading” so

that speakers near the edges of the array contribute less to the overall sound field than speakers

near the centre of the array - much like using a windowing function when undertaking an Fourier

Transform on time series data to obtain frequency domain data.

D. Focussing the Cancelling Sound Field - Audio Spotlight

30

The audio spotlight27-30 is a relatively new invention that uses airborne ultrasonics and the

non-linearity of the air motion generated by the propagating wave to generate sound in the audio

frequency range. The frequency content of the sound wave can be tailored by appropriate

modulation of the ultrasonic wave, which is achieved by passing it through an electronic control

box. The most interesting part is that the sound generated in the audio frequency range is

contained within the confines of the ultrasonic beam and thus shares the same spreading

properties as the ultrasonic beam. Due the high frequency of the ultrasound and corresponding

short wavelength compared to the size (approximately 300 mm) of the transducer generating it,

the beam spreads out at an angle of approximately 3 degrees, allowing for a highly directional

audio signal as well.

The audio spotlight has similar properties to an end fired speaker array shown in Figure 20,

with the audio sound level gradually increasing until it reaches the “Rayleigh distance” which

is typically 1.5 m from the transducer array for the two commercially available systems

illustrated in Figure 21. The one on the right has had some of the black covering removed to

reveal the PVDF film transducers which make it up.

The measured directivity of an acoustic spotlight for a 500 Hz audio tone and a 48 kHz

carrier is illustrated in Figure 22.

Unfortunately, it may be shown (see the following table calculated using the analysis of

Berktay28) that the level of ultrasonic sound pressure needs to be tens of decibels above the audio

sound pressure level that is to be generated and this clearly would be dangerous if reasonably

high industrial noise levels are to be controlled. The other problem is that as ultrasonic levels

become higher, more of the ultrasonic energy is spent heating the air and eventually a point is

reached at which no increase can be achieved in the intensity of the audio field. The level at

which this occurs is not clear at this time but from the following table, it can be seen that

31

excessive ultrasonic levels are needed even to achieve an 80 dB noise level at low frequencies.

Thus this approach to local cancellation is unlikely to be practical for industrial applications,

except in reasonably quiet environments where an annoying tone is to be attenuated or if the

environment needs to be quieter for reasons of comfort or work efficiency.

One of the problems associated with an audio spotlight is the harmonic distortion at low

frequencies. This is illustrated in Figure 23 where both ultrasonic and audio levels are shown as

a function of axial distance from the transducer for a 500 Hz audio signal, where it can be seen

that the measured ultrasonic levels are slightly lower than predicted in Table 1.

E. Steering the Virtual Sensor Location and the Cancelling Sound Field Focal

Point

Whether the sound pressure sensor at the observer’s ear is virtual or real, if the observer’s

ear moves at all, there will be a diminution in the noise reduction achieved and if the ear moves

back and forth, the perception will be a rapidly varying noise which would be very annoying.

Thus, it is necessary to track the observer’s head position and steer the control source beam and

virtual sensor location to keep track. If a physical sensor array is used, the steering of the virtual

sensor location could be achieved with look up tables and a smoothing algorithm to avoid sudden

changes in control parameters. The steering of the controlling sound field, which is generated

by a loudspeaker array, could be achieved in the same way.

F. Tracking the Head Position

32

In order to know where the sound field is to be focussed and where the virtual sensor is to

be located, it is necessary to be able to track the location of the head around which the sound

field is to be reduced. This could be done using three ultrasonic transmitter/receivers embedded

in he cabin ceiling and some sophisticated software that excludes reflections from objects other

than the head or alternatively, a transmitter could be worn by the operator. In practice, a

combination of the two techniques will most likely be the best. The transmitter could be in a

head rest and the ultrasonic transmitter/receivers could be limited in how far from the headrest

transmitter they would track the head. Work using this approach is currently in progress.

6. CONCLUSIONS

Current successful applications of active noise control systems in industry outside of the

defence sector are essentially limited to the control of sound propagating in ducts and principally

to the control of plane wave propagation only. By use of an example, the development effort and

complexities associated with developing and installing a multi-modal active noise control system

in an industrial environment were discussed. It is clear that if active noise control systems are

to be widely used in industry, there needs to be quantum advances made in the following areas:

• simplicity of controller use

• reduction in control system cost

• control system capability and robustness

• transducer cost reduction and robustness

• sensing and generation of cancelling sound fields

7. ACKNOWLEDGEMENTS

33

In assembling the material for this paper, the author gratefully acknowledges assistance from

his colleagues, Dr Anthony Zander, Dr Xun Li, Dr Damien Leclercq, Mr George Vokalek, Dr

Xiaojun Qiu, Mr Dick Petersen and Ms Laura Brooks.

8. REFERENCES

1. C.H. Hansen, “Active noise control - from laboratory to industrial implementation,” Proc.

Noise-Con '97 (1997), pp. 3-38.

2. R. Hinchliffe, I. Scott, M. Purver and I. Stothers, “Tonal active control in production on a

large turbo-prop aircraft. Proc. Active 2002, (2002), pp. 369-376.

3. C.H. Hansen, Understanding Active Noise Cancellation. (Spon Press, London, 2001).

4. X. Qiu and C.H. Hansen, “A comparison of adaptive feedforward control algorithms for

multi-channel active noise control,” Proc. WESPAC8, Paper ME41, 8 pages.

5. S.D. Sommerfeldt and J. Tichy, “ Adaptive control of a two-stage vibration isolation mount,”

J. Acoust. Soc. Am. 88, 938-944 (1990).

6. F. Fohr, C. Carme, J-L. Peube, P. Vignassa and F. Le Brazidec, “Active exhaust line for truck

diesel engine”, Proc. Active 2002, (2002), pp. 327–332.

7. R. Boonen and P. Sas, “Design of an active exhaust attenuating valve for internal combustion

engines,” Proc. Active 2002, (2002), pp. 345–356.

8. D.R. Morgan, “An analysis of multiple correlation cancellation loops with a filter in the

auxiliary path,” IEEE Trans. Acoust., Speech and Sig. Proc., ASSP-28, 454-467 (1980).

9. C.R. Fuller and J. Carneal, “A biologically inspired control approach for distributed elastic

systems,” J. Acoust. Soc. Am. 93, 3511–3513 (1993).

34

10. C.R. Fuller, M.R.F. Kidner, X. Li. and C.H. Hansen, “Active-passive heterogeneous blankets

for control of vibration and sound radiation”, Proc. Active 2004, (2004), paper A04-91.

11. A.J. Bullmore, P.A. Nelson, A.R.D. Curtis and S.J. Elliott, “The active minimization of

harmonic enclosed sound fields, Part II: Computer simulation,” J. Sound Vib. 117, 15-33

(1987).

12. S.J. Elliott, A.R.D. Curtis, A.J. Bullmore and P.A. Nelson, “The active minimization of

harmonic enclosed sound fields, Part III: Experimental verification,” J. Sound Vib. 117, 35-

58 (1987).

13. P.A. Nelson, A.R.D. Curtis, S.J. Elliott and A.J. Bullmore, “The active minimization of

harmonic enclosed sound fields, Part I: Theory,” J. Sound Vib. 117, 1–13 (1987).

14. S. Sommerfeldt and P. Nashif, “A comparison of control strategies for minimising the sound

field in enclosures,” Proc. Noise-Con ‘91, (1991), pp. 299–306.

15. S.J. Elliott and A. David, “A virtual microphone arrangement for local active sound control,”

Proc. 1st International Conference on Motion and Vibration Control, (1992), pp. 1027-1031.

16. J. Garcia-Bonito, S.J. Elliott and C.C. Boucher, “Generation of zones of quiet using a virtual

microphone arrangement,” J. Acoust. Soc. Am., 101, 3498–3516 (1997).

17. S.R. Popovich, 1997, “Active acoustic control in remote regions,” US Patent No. 5701350,

(1997).

18. A. Roure and A. Albarrazin, “The remote microphone technique for active noise control,”

Proc. Active ‘99, (1999), pp. 1233-1244.

19. E. Friot, A. Roure and M. Winninger, “ A simplified remote microphone technique for active

control at virtual error sensors,” Proc. Internoise 2001, (2001), pp.681-684.

20. C.D. Kestell, C.H. Hansen and B.S. Cazzolato, “Active noise control in a free field with

virtual sensors,” J. Acoust. Soc. Am., 109, 232–243 (2001).

35

21. C.D. Kestell, C.H. Hansen and B.S. Cazzolato, “Virtual sensors in active noise control,”

Acoust. Australia, 29, 57–61 (2001).

22. C.D. Kestell, B.S. Cazzolato and C.H. Hansen, “Active noise control with virtual sensors in

a long narrow duct,” Int. J. Acoust. and Vib., 5, 63–76 (2000).

23. J.M. Munn, “Virtual Sensors for Active Noise Control,” PhD Thesis, University of Adelaide,

South Australia, (2004).

24. B. Cazzolato, “An adaptive LMS virtual microphone,” Proc. of Active, 2002 (2002), pp. 105-

115.

25. H.J. Gawron and K. Schaaf, “Active cancellation of harmonics using virtual microphones,”

Proc. 2nd International Conference on Vehicle Comfort: Ergonomic, Vibrational, Noise and

Thermal Aspects, (1992), pp. 739-748.

26. C.H. Hansen, D.A. Stanef and R.C. Morgans, “Real time control of sound pressure and

energy density in a mining vehicle cabin,” Proc. 10th International Congress on Sound and

Vibration (2003), pp. 3713-3722.

27. M.B. Bennett and D.T. Blackstock, “Parametric array in air”, J. Acoust. Soc. Am., 57, 562-

568 (1975).

28. H.O. Berktay, “Possible exploitation of nonlinear acoustics in underwater transmitting

locations,” J. Sound Vib. 2, 435–461 (1965).

29. F.J. Pompei, “The use of airborne ultrasonics for generating audible sound beams,” J. Audio

Eng. Soc. 47, 726–730 (1999).

30. P.J. Westervelt, “Parametric acoustic array,” J. Acoust. Soc. Am., 35, 535-537 (1963).

Table 1. Ultrasonic noise levels (dB re 20 µPa) required to produce 80 dB and 100 dB,

respectively, at the face of the transducer (Courtesy Laura Brooks). fc is the ultrasonic carrier

frequency and the audible frequency is shown in the second row of the table.

Audible

SPL

80 dB 100 dB

fc (Hz) 50 Hz 200 Hz 500 Hz 1 kHz 50 Hz 200 Hz 500 Hz 1 kHz

30k 152 140 132 126 162 150 142 136

48k 158 146 138 132 168 156 148 142

60k 161 149 141 135 171 159 151 145

Figure Captions

Figure 1. Gear wheel and tacho mounted on the fan shaft.

Figure 2. Microphone holder with air purge.

Figure 3. Error sensor locations.

Figure 4. Duct with speakers attached.

Figure 5. Speaker locations for multi-mode control.

Figure 6. Schematic of controller hardware.

Figure 7. Spray dryer system active noise controller front panel layout.

Figure 8. Typical spectra in the community with and without active control switched on.

Figure 9. Controller Architecture.

Figure 10. Multi-rate filtering controller (5 error inputs and 4 control outputs), which allowsthe extension of performance to achieve significant noise reductions over a number of octavebands. Only the details for one octave band are shown - all other bands would be representedin an identical way and for each output channel, the outputs from each band are combinedprior to being sent to the actuator.

Figure 11. Illustration of controller delays for various real and down sampled rates. The downsampled rate is shown on the abscissa and the digital divide number is shown parametrically.

Figure 12. Acceleration response of a bare cantilever beam, a beam with a thick layer of foamcontaining five embedded, non-driven inertial actuators, with the actuators driven using fivesingle channel controllers and then driven with a 5-out.6-in fully coupled (MIMO) controller.

Figure 13. 4-microphone energy density sensor.

Figure 14. Optical, energy density sensor manufactured by Phon-Or Ltd.

Figure 15. Illustration of Elliott and David’s (1992) virtual microphone. The subscript, p/v,represents the pressure at the virtual microphone due to the primary source, p/a represents thepressure at the actual microphone due to the primary source. Hat symbols represent estimatedquantities and the S quantities represent transfer functions in the frequency domain betweenthe controller output and the error sensor input for the actual microphone (subscript a) andthe virtual microphone (subscript v).

Figure 16. Illustration of Roure and Albarrazin’s (1999) virtual microphone. The differencebetween this figure and the preceding one is the transfer function H, which affects theequations as well.

Figure 17. Illustration of linear and quadratic extrapolation from the physical microphones,p1, p2 and p3 to the virtual location, pv.

Figure 18. Adaptive LMS microphone [20].

Figure 19. Focussing controlled sound fields with curved arrays.

Figure 20. Parametric array, showing how it behaves as a virtual end fired speaker array.

Figure 21. Commercially available audio spotlights.

Figure 22. Measured directivity of acoustic spotlight at 500 Hz (Courtesy Laura Brooks).

Figure 23. Spotlight sound levels - both ultrasonic and audible.

Figure 1. Gear wheel and tacho mounted onthe fan shaft.

O rings

Aluminum foil

Acousticfoam

Stainlesssteel

mount

Duct wall

AirFlow

NylonHolder

WeldElectret

Microphone

Clamp

Vyon attenuator

Cooling Air Duct

Figure 2. Microphone holder with air purge.

4,9

5,10

1,6 60° 60°

40°

2,7,11 3,8,12

Figure 3. Error sensor locations.

Figure 4. Duct with speakers attached.

EL 33850

EL 33500

EL 32700

EL 32300

A A

B B

C C

D D

Sect AA

Sect BB

Sect CC

Sect DD

60°

30°

Figure 5. Speaker locations for multi-mode control.

A/D & D/A

A/D & D/A

A/D & D/A

CONNECTOR

CONNECTOR

CONNECTOR

EMERALD

EMERALD

BAC

KP

LAN

ERS232

1 x ANALOG IN1 x ANALOG OUT1 x ANALOG IN1 x ANALOG OUT

1 x ANALOG IN1 x ANALOG OUT

13 CHANNELSTOTAL

Figure 6. Schematic of controller hardware.

Tool bar System status

Figure 7. Spray dryer system active noise controller front panel layout.

SPL measurementSPL history Function panels

Menu bar

10

15

20

25

30

35

40

45

50

100 150 200 250 300 350 400

F requency (H z)

bef ore A NC af ter A NC

noise reduc tion: 14.58 dB at BPF of 182 Hz

Figure 8. Typical spectra in the community withand without active control switched on.

Figure 9. Controller Architecture.

Digitalfilter

Cancellation pathtransfer function

modeller

Adaptivealgorithm

5Error

signals(inputs)

Referencesignal(input)

4Controlsignals

(outputs)

20 models

Octavebandfilter

Octavebandfilter

Octavebandfilter

Octavebandfilter

Octavebandfilter

Octavebandfilter

Ch. 1 out

Ch. 2 out

Ch. 3 out

Ch. 4 out

sum over all octave

bands

weight update coefficients

Σ

Σ

Σ

Σ

Σ

Figure 10. Multi-rate filtering controller (5 error inputs and 4 control outputs), whichallows the extension of performance to achieve significant noise reductions over a numberof octave bands. Only the details for one octave band are shown - all other bands would berepresented in an identical way and for each output channel, the outputs from each bandare combined prior to being sent to the actuator.

-80-70

-60-50

-40-30

-20-10

0

50 100 150 200 250 300 350 400 450 500

Frequency (Hz)

Aut

o-sp

ectr

um (d

B re

1m

/s2/

V)

bare beam passive

5 by 5 SISO 5 by 6 MIMO

Figure 11. Acceleration response of a bare cantileverbeam, a beam with a thick layer of foam containing fiveembedded, non-driven inertial actuators, with theactuators driven using five single channel controllers andthen driven with a 5-out.6-in fully coupled (MIMO)controller.

0 .1

1

1 0

1 0 0

1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0ac tual s am ple rate (Hz )

time

dela

y (m

s)

a t 5 5 H za t 2 5 H za t 1 1 0 H za t 2 0 0 H z

divide by divide by divide

Figure 12. Illustration of controller delays for various real and downsampled rates. The down sampled rate is shown on the abscissa and thedigital divide number is shown parametrically.

Figure 13. 4-microphone energydensity sensor.

Figure 14. Optical, energy density sensormanufactured by Phon-Or Ltd.

app /ˆ

vpp /ˆvp

ap

1∑

∑

aS

vS

vp

vxcontrolsignal

actuatorphysical

microphonevirtual

microphone

+

+ +

apvp pp // ˆˆ =

acp /ˆ

cq

cvvpv

caapaqSpp

qSpp

+=

+=

/

/apvp pp // =

cvcaa

cvapv

qSqSp

qSppˆˆ

ˆˆˆ /

+−=

+=⇒

vcp /ˆ

Figure 15. Illustration of Elliott and David’s (1992) virtual microphone. Thesubscript, p/v, represents the pressure at the virtual microphone due to theprimary source, p/a represents the pressure at the actual microphone due tothe primary source. Hat symbols represent estimated quantities and the Squantities represent transfer functions in the frequency domain between thecontroller output and the error sensor input for the actual microphone(subscript a) and the virtual microphone (subscript v).

app /ˆ

vpp /ˆvp

ap

∑

∑

aS

vS

vp

vxcontrolsignal

actuatorphysical

microphonevirtual

microphone

+

+ +

acp /ˆ

cq

cvvpv

caapaqSpp

qSpp

+=

+=

/

/ ⇒

vcp /ˆ

( ) svsaa

svapv

qSqSpH

qSpHpˆˆˆ

ˆˆˆˆ /

+−=

+=

H

apvp pHp //ˆ=

apvp pp // ≠

Figure 16. Illustration of Roure and Albarrazin’s [ virtual microphone. Thedifference between this figure and the preceding one is the transfer function H,which affects the equations as well.

p1 p1

p2 p2 p3

pv

pv

xv xv2h hh

Linear Extrapolation Quadratic Extrapolation

322212 2

)2)(()2(

2

)(p

h

hxhxp

h

hxxp

h

hxxp vvvvvvv

+++

++

+=12 2

12

ph

xp

hx

p vvv −⎟⎟

⎠

⎞⎜⎜⎝

⎛+=

Figure 17. Illustration of linear and quadratic extrapolation from the physicalmicrophones, p1, p2 and p3 to the virtual location, pv.

controlsignal

actuator

physicalmicrophone

arrayvirtual

microphone

xv

pvp1ap2ap3a

qchh

[ ]T321321ˆ aaaoptoptoptoptv pppwwwp == pw

][ˆ 2eminppe vv ⇒−=

Figure 18. Adaptive LMS microphone [24].

Figure 19. Focussing controlled sound fieldswith curved arrays.

Ultrasonic Transducer / Parametric Array

End-fired Virtual ArrayRayleigh Distance

Audible Sound Beam

Figure 20. Parametric array, showing how itbehaves as a virtual end fired speaker array.

Figure 21. Commercially available audiospotlights.

0 dB

-10 dB

-20 dB

-30 dB

0 dB

-10 dB

-20 dB

-30 dB

Figure 22. Measured directivity of acousticspotlight at 500 Hz (Courtesy Laura Brooks).

Ultrasonic carrier

Fundamentalaudible

Soun

d pr

essu

re le

vel (

dB re

20

Pa)

µ

Distance from sound source (m)

First harmonic

Axial SPL (dB re 20 Pa)µ140

130

120

110

100

90

80

70

60

500 0.5 1 1.5 2 2.5 3 3.5

Figure 23. Spotlight sound levels - bothultrasonic and audible.