Active headrest

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An active headrest is a safety device in automotive design that automatically adjusts the position of the headrest during a collision to reduce the risk of whiplash injuries by minimizing the relative motion between the occupant's head and the vehicle seat.

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Key research themes

1. How do occupant seat positions and postures affect head and neck injury risks and discomfort in vehicle crashes?

This research theme investigates the influence of varied occupant postures, especially reclined and nonstandard seating (including forward head posture and rotated head postures), on biomechanical responses, injury risks (e.g., head injury, whiplash), and perceived discomfort during vehicle crashes. Understanding occupant kinematics and muscle activity is critical for designing seats and safety systems that mitigate injury in diverse real-world seating scenarios encountered in modern and autonomous vehicles.

Influence of a Passenger Position Seating on Recline Seat on a Head Injury during a Frontal Crash

by Jacek Cebula

2023, Sensors

Key finding: This study found that increased seatback recline angles (up to −117°) substantially affect head accelerations and head injury criteria (HIC) during frontal crashes. The authors highlight the inadequacy of current ATD dummies... Read more

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Role of Rotated Head Postures on Volunteer Kinematics and Muscle Activity in Braking Scenarios Performed on a Driving Simulator

by Tobias Siebert

2024, Annals of Biomedical Engineering

Key finding: This experimental study demonstrated that occupants with rotated head postures (~63°) exhibited asymmetrical neck muscle activation and altered head kinematics during braking, implicating increased vulnerability to... Read more

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Forward Head Posture in Young Adults: A Systematic Review

by Ayesha Arooj

2023, THE THERAPIST (Journal of Therapies & Rehabilitation Sciences)

Key finding: The review identified a high prevalence (up to 62.3%) of forward head posture among young adults, attributed to prolonged neck flexion during activities such as device usage. Forward head posture is associated with increased... Read more

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2. What are the effects of seat design and suspension systems on occupant comfort, muscle fatigue, and vibration transmission during prolonged vehicle use?

Focusing on the interplay between seat construction (foam stiffness, back support height, suspension) and biomechanical responses, this research theme explores how design parameters affect driver and passenger comfort, neuromuscular fatigue, postural stability, and vibration transmission. These factors are crucial for reducing discomfort and injury, especially during long-duration drives or in autonomous vehicles where occupants spend extended periods seated with varied postures.

Car seat impact on driver’s sitting behavior and perceived discomfort during prolonged real driving on varied road types

by Clément Bougard

2022, PLOS ONE

Key finding: This real-driving study showed that seats equipped with a suspension system (S3) significantly reduced local perceived discomfort, particularly in buttocks and thighs, compared to soft and firm seats without suspension,... Read more

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Perceived discomfort and neuromuscular fatigue during long-duration real driving with different car seats

by jean-marc Allègre

2023, PLOS ONE

Key finding: During 3-hour continuous driving, all tested seats showed increased whole-body discomfort correlated with neuromuscular fatigue indicated by electromyographic variability chiefly in cervical and lumbar muscles. Despite... Read more

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Effects of seat back height and posture on 3D vibration transmission to pelvis, trunk and head

by Riender Happee

2025, arXiv (Cornell University)

Key finding: Using wide-band 3D vibration stimuli on commercial car seats, the study found that low back support height reduced head motion but was perceived as less comfortable. Different sitting postures (erect, slouched, head down)... Read more

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Gender and Anthropometric Effects on Seat-to-Head Transmissibility Responses to Vertical Whole-Body Vibration of Humans Seated on an Elastic Seat

by Krishna Dewangan

2023, Vibration

Key finding: Analysis of 58 subjects revealed significant gender-related differences in seat-to-head transmissibility (STHT) of vibrations, modulated by presence of back support and excitation levels. Females exhibited generally lower... Read more

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3. How can active/headrest-based noise and motion control technologies enhance occupant comfort and safety in vehicles and other transport modes?

This theme investigates technological innovations in active noise control (ANC) systems and monitoring tools integrated into vehicle seats and headrests. It includes adaptive algorithms to create quiet zones, assessment methods of occupant head positions via sensors, and multi-modal monitoring systems capable of tracking vital signs. These approaches aim to reduce noise and vibration discomfort, improve physiological monitoring, and thereby enhance occupant well-being and safety, particularly relevant for autonomous and highly automated vehicles.

Active Noise Control for Aircraft Cabin Seats

by Ignazio Dimino

2023, Applied Sciences

Key finding: The implementation of a two-input-two-output filtered-X LMS adaptive control system with speakers embedded at the passenger's headrest achieved localized sound attenuation up to 20 dB at low frequencies pertinent to turboprop... Read more

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A Virtual Sensing Active Noise Control System Based on a Functional Link Neural Network for an Aircraft Seat Headrest

by alberto erspamer

2023, Journal of Vibration Engineering & Technologies

Key finding: This study introduced a virtual sensing ANC system utilizing a functional link neural network to estimate and cancel acoustic pressures beyond physical microphone locations. The approach effectively extended quiet zones... Read more

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Automated recognition of rear seat occupants' head position using Kinect™ 3D point cloud

by Suzanne Cross

2022, Journal of safety research

Key finding: By employing Microsoft Kinect™ 3D point cloud data and algorithmic skeletal tracking, the study automated detection of rear-seat child occupant head positions during naturalistic driving. The system achieved 41% accuracy in... Read more

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A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver’s Vital Signs

by Pujitha Gunaratne

2024, Sensors

Key finding: A prototype cushion embedding four sensor modalities (capacitive ECG, reflective photoplethysmography, magnetic induction measurement, seismocardiography) demonstrated successful continuous heart and respiratory rate... Read more

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All papers in Active headrest

Active Noise Control proposal design enhanced because of using sensors/actuators based on nanostructures

by Oscar Melgarejo

2023, E3S Web of Conferences

Big machines and motors, that under operating work produces high decibels of noise in wide range of work, these values are near 90 dB that is not healthy for humans. Normally, it is used passive mechanisms to attenuate noise such as big... more

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Optimisation of Active and Semi-Active Noise and Vibration Control Systems

by Colin Hansen

2022

Active and semi-active noise control system design may be considered to be multi-variable optimisation processes. The performance of the final design is a function of the order in which various aspects of the design are optimised as well... more

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Active Noise Control proposal design enhanced because of using sensors/actuators based on nanostructures

by jorge alencastre

2021, E3S Web of Conferences

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Acoustic Active Noise Control System

by Moisés Piedade

2021

Acoustic active noise control is a technique in which ambient noise is acquired by a microphone, processed, and emitted by a loudspeaker with opposite phase from the original. When the two signals are summed the noise level it will be... more

Figure 1. A Basic Active Noise Control System. Mean Squares), Filtered-X LMS (FXLMS) [2, 3, 4] and multi-channel FXLMS algorithms [5] for a feedforward, broadband, active noise control system. The basic active noise control system is illustrated in Figure 1. It is composed by a signal processor, two microphones (reference and error) and a loudspeaker. It works by modeling the primary path, from the reference microphone to the error microphone and the secondary path from loudspeaker to the error microphone, with adaptive filters.

Figure 3. Electrical noise reduction system. Classical noise reduction methods (Figure 3) are usually simpler than the referred ones because there is no secondary path; the noise is subtracted directly from the original signal. In these systems, the basic approach is to use a digital FIR (finite impulse response) filter, like the one shown in Figure 2, and the LMS algorithm (Eq. 1).

Figure 4. Four-Channel FXLMS algorithm with feedback neutralization. In Figure 5, P(z), S(z) and F(z) represent the transfer functions of the primary, secondary and feedback paths, respectively. S(z) and F(z) represent FIR models for S(z) When an acoustic sensor is used for acquiring the

reference signal, this signal is corrupted by the anti-noise generated by the loudspeaker, producing acoustic feedback. A feedback neutralization filter, F(z), as is shown in Figure 5, can solve this problem [9, 10]. Figure 5. Active noise control system with feedback neutralization using FX LMS algorithm.

Figure 7. The Active Noise Control System. The four-channel system is represented in Figure 7 and To obtain a larger cancellation region it is required using a multi-channel ANC [5]. We have decided to build a system based on four noise canceling loudspeakers, excited by the signals produced by the algorithm represented in Figure 4. This system uses the muti- channel FX LMS algorithm represented in Eq. 3.

The first is a one-channel system implemented with a DSP32C [12] 32 bits floating-point DSP. It had only one reference microphone, one error microphone and one loudspeaker. It served as simpler starting point.

Figure 8. Four-channel active noise control system. n the final system, we used a DSP board developed by some colleges, based on the TMS320C50. The board has eight 13 bits analog input and output channels implemented with the A/D LM12548 and the D/A MAX458), several digital input/output ports, serial interface port, expansion bus and three external memory banks. The interface to the PC is done through aX DS510 system [11]. The TMS320C50 has a clock frequency of 40 MHz (although only 50 ns instructions cycle). The maximum sampling rate achieved was 4 kHz, for the four-channel ANC system, using filters with 32 coefficients. The four-channel system with all boards visible is presented in Figure 8.

In this system, the loudspeakers and error microphones were arranged as in Figure 9. The noise source used was the recorded noise from the driver's seat of a car in a highway, traveling at about 120 km/h. We experimented with several versions of the LMS algorithm, namely the standard LMS algorithm, the normalized LMS algorithm and the correlation LMS algorithm [6, 7, 8]. The results obtained are shown in Table 1.

Table 1. Total Noise reduction Figure 10. Noise level spectrum, with and without ANC, for the one-channel system when the original noise is acquired inside a running car. An 8.1-dB total noise reduction was obtained.

In Figure 12 and Figure 13, two examples of the filters impulse responses that model the secondary paths are shown. It can be seen that they both have a strong peak, roughly representing a delay.

In the four-channel system, the loudspeakers and error microphones were arranged as in Figure 11.

Figure 15. Noise amplitude error microphone #4, with and without ANC, when the original noise comes from the interior of a bus. A 9.3-dB total noise reduction was obtained. In these experiments, the pre-recorded noise was taken from the inside of a moving bus in a highway, at a passenger's seat. The bus was traveling at about 100 km/h.

Figure 16. Noise level spectrum at error microphone #3, with and without ANC, when the original noise comes from the interior of a bus. A total 8.8-dB noise reduction was obtained.

Table 3 shows the noise reduction values obtained in two similar experiments.

Table 2. Total noise reduction in the error microphones

Table 3. Total noise reduction in the error microphones

Table 5. Noise reduction for different FIR filter length Finally, we tried different acoustic arrangements increasing the distance from the secondary loudspeakers about 25 cm to the error microphones, but still taking care in their position, and got similar results. It should be noted that an increase of 25 cm corresponds to a three samples delay (for a 4.0 kHz sampling frequency and taking the speed of sound as 340m/s) in the impulse response of the secondary path modal filters. This isn’t very significant and explains the similar results.

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Design of Remote Quiet Zones Using Spot-Type Sound Reducers

by Romain Boulandet

2021, Acta Acustica united with Acustica

This paper presents a local control approach to generate remote quiet zones. To deal with situations where global control can hardly be achieved, it is proposed to use an arrangement of spot-type sound reducers as originally suggested by... more

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A simplified method for online acoustic feedback path modeling in multichannel active noise control systems

by Wataru Mitsuhashi

2021, SICE Annual Conference

This paper investigates the issue of acoustic feedback in multichannel active noise control (ANC) systems. The presence of acoustic feedback degrades the performance of the ANC systems, and in the worst case the ANC system may become... more

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Ear Field Adaptive Noise Control for Snoring: An Real-time Experimental Approach

by IEEE/CAA J. Autom. Sinica

2019, IEEE/CAA Journal of Automatica Sinica

In this paper, an active noise control (ANC) system is developed to provide an effective and non-intrusive solution for reducing loud snoring to provide a quiet environment for a snorer's bed partner. An adaptive least mean square (LMS)... more

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REFERENCE SIGNAL ESTIMATION IN ACTIVE NOISE REDUCING SYSTEM OF A WASHING MACHINE

by Stanislaw Wrona

2018

Noise generated by devices is an important problem in numerous environments. It can be successfully reduced by controlling vibration of their casings. Efficiency of this method has been confirmed by the authors in previous publications. A... more

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IEEE Listening in a Noisy Environment Integration of active noise control in audio products

by Peng Lee

2018

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Real time evaluation of soft microphones on a local active noise control system

by Maria Rangoussi and

2016, The Journal of the Acoustical Society of America

Systems for the Active Control of acoustic Noise (ANC) rely on measurements of the noise signal in order to produce the required "quiet zone". Soft Microphones (SM) are a virtual microphone alternative already proposed by the authors,... more

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Adaptive Signal Processing Systems for Active Control of Acoustic Noise on Circular Paths

by Waleed Abdulla

2016

Adaptive active noise control (ANC) systems create a silent point at the location of an error microphone. This silent point can creates a surrounding zone of quiet with small dimensions. Moreover, the error microphone has to be located... more

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Active noise control systems

by Roy Leitch

2016, Physical Science, Measurement and …

A retrospective review of the development of active noise control systems is presented, arguing that the design of active noise control (ANC) systems should be considered from a control systems point of view. This approach provides a... more

Fig.14 Transfer characteristics of secondary source plus microphone a Amplitude h Phace

To verify eqns. 39a and b the same experimental arrangement as in Fig. 12 was used. The observer micro- phone was placed opposite to the primary source with r, = 2m and the separation between the sources was varied so that Ar = 0.385m was obtained at d = 1.3 m. The sources were driven by a PRBS signal and adjusted so that a power ratio a of —6.0 dB was obtained over a range of 0-5 kHz at the observation point. Substituting these parameters and c = 340 m/s in eqns. 39 yields the frequencies of the limit of cancellation as

Fig.18 Schematic diagram of feedback control structure The basic feedback control structure (FBCS) is shown schematically in Fig. 18. The primary source emits an

Active noise control was one of the earliest applications of electronics to the control of physical systems. Lueg filed for a patent in Germany in 1933 and in the USA in 1934 and was granted US Patent 2043416 in 1936 [8]. In his patent, Lueg made use of the two basic principles of realising an ANC system: interference and absorption. The interference principle results from the arbitrary physical mixing of acoustic waves resulting in construc- tive and destructive interference which, in turn, cause intensification and weakening of the sound field, respec- tively. He attempts to manipulate this principle of super- position so that the destructive interference of sound waves could be used to eliminate unwanted noise. He introduced the concept of active attenuation of sound by using artifically produced acoustic waves mixed with the unwanted sound so that the waves were in antiphase, and destructive interference resulted by design. Lueg also uses the absorption principle by synchronising the movements of a loudspeaker’s diaphragm in antiphase to the unwanted noise so that the noise energy is absorbed by the loudspeaker. Lueg’s proposals for obtaining destruc- tive interference and sound energy absorption are shown in Fig. 1, which is taken from the illustration page of his patent. Fig.1 — [llustrations from Lueg’s patent

Fig. 3. Performance of Olson’s electronic sound absorber at micro- phone location The performance of the Olson device as a ‘sound pres- sure reducer’ at the detector location is shown in Fig. 3.

Olson’s absorber is shown in Fig. 2. The microphone detects the unwanted noise and feeds it to the amplifier. The amplifier, in turn, drives the loudspeaker such that the sound pressure at the microphone location is reduced. This effectively creates a ‘zone of cancellation’ in front of the absorber. As might be expected from the advance of technology in the 1950s, Olson’s device shows considerable sophistication and ingenuity. In fact, it was possible to control the phase with good accuracy over a reasonably broad frequency range. However, the basic feedback structure of the system, along with other system errors, limits the utility of Olson’s device.

Fig. 4 Performance of Olson's device at various distances relative to microphone a 10 cm (4 in) 6 25 cm (10 in) pet ney f SAK The high-frequency performance of the system is limited by the feedback control structure of the system and the limited electronic capabilities, whereas the low-frequency performance limitation is due to the response of the loud- speaker in that range. Nevertheless, the performance of the device as measured at the microphone location is good. Unfortunately, as shown in Fig. 4, both the

There has been considerable effort devoted to duct noise since Lueg’s invention and primarily since Jessel’s first work [11, 12, 25, 26, 30-72]. These efforts have resulted in the development of various systems, depicted in Fig. 6, for duct noise. The acoustic monopole system is Fig.6 Types of active noise attenuators for duct noise a Monopole (Lueg) b Tripole (Jessel) c Dipole (Swinbanks) d Dual monopole (Chelsea system)

where c is the velocity of sound in the medium. The attenuating factors H, and H,, on the other hand, are inversely proportional, respectively, to the square of the distances r, and r,. and the autopower spectral densities can be written as (108, 109]

Fig. 7 Primary and secondary sources emit acoustic waves to produce cancellation at point O Gcco(@) < Gppo(w) For the purpose of a quantitative description of the phe- nomenon of cancellation we shall define the field cancel- ation factor K as the ratio of the cancelled spectrum Gppo(w) — Gcco(@) to the primary spectrum Gppo() which existed at the given point O prior to the super- position of the secondary field s,(t); i-e.

Fig. 10 Field cancellation factor as related to the power ratio and cross-spectral density factor of primary and secondary waves If B is treated as a parameter in eqn. 13, the field can- cellation factor K as a function of power ratio « can be represented as a family of curves, each curve correspond- ing to a particular value of B, as shown in Fig. 10.

Fig. 9 Shaded region represents —B values for which cancellation occurs for a certain This is the phase condition for cancellation and is shown in Fig. 9. The amplitude condition for cancellation can also be obtained from eqn. 14. As shown in Fig. 9, —f lies between the functions U and L. However, from the defi- nition of 8, the cross-spectral density factor is bounded by one; i.e.

from which it is clear that wt — 6(w) represents an angle either in the second or third quadrant, including the x axis. Solution of eqn. 28 for wt — 6(w) yields that for the secondary field to cancel the primary field the phase dif- ference wt — @(w) between the same-frequency com- ponents of the waves is required to be Fig. 11 Range of difference in phase between primary and secondary waves for cancellation Using the relationship between the velocity of sound, the signal wavelength and the signal frequency, eqn. 27 can be written as

Fig.12 Experimental set up for verification of general conditions of active noise cancellation tiometers, one NAIM power amplifier and a condenser- type Cirrus microphone. the amplifier used to amplify the microphone’s output. The amplifier does not have a significant effect on the overall transfer characteristics of Fig. 14, whereas the effect of the acoustic path on the phase is clear from Fig. 14. Within the range 60-900 Hz the loudspeaker- microphone combination has an almost flat transfer characteristic. In the range 900-2800 Hz the amplitude characteristic varies between —3.5 and +3.5 dB. This variation reflects on the phase characteristic as a variable phase with an average equal to the linear phase charac- teristic resulting from the time delay of the acoustic path. The loudspeaker response diminishes above 4.2 kHz. facilities for single- and dual-channel measurements. The analytical procedure of the device is based on the fast Fourier transform (FFT) through which the normal frequency-domain analysis functions, such as autopower, synchronous spectrum, crosspower, transfer function, coherence and power cepstrum with a maximum fre- quency of 30 kHz, are provided. The time-domain analysis functions provided by the instrument, via inverse FFT, are the time waveform, impulse response, autocor- relation and crosscorrelation functions. The instrument generates three types of test signals, namely PRBS (pseudo-random binary sequence) signal, periodic multi- sine signal and impulse signal. Among the input/output facilities of the instrument are two analogue input chan- nels, digital plotter, analogue plotter and video signal output. The instrument’s interface unit is the IEEE Stan- dard 488 (1978) with full remote control.

between the primary and secondary waves, of the same frequency, reaching the observation point. Thus it will provide a basis for measuring (unknown) separation between the primary and secondary sources. The family of cancellation curves in Fig. 10 was veri- fied using a fixed frequency and varying the value of the power ratio for each distance difference value. The results of such an experiment using a fixed frequency of 100 Hz agree with Fig. 10 to within experimental accuracy.

Fig. 20 Schematic diagram of feedforward control structure ondary source to be superimposed on the unwanted noise. The aim is to reduce the sound pressure level to zero at an observation point which is at a distance of r, relative to the primary source and a distance r, relative to the secondary source.

Fig. 25 = Transfer characteristics of DFC compared to H(z) a Amplitude b Phase constant-length transmission line. Thus the maximum cancellation condition is satisfied for only one frequency J, (163 Hz). Noise components at frequencies smaller or

Fig. 24 Offset in controller characteristics from required values a Amplitude error b Phase error processor using 192 instructions and setting the cutoff frequencies of the lowpass antialiasing and reconstruction filters at 16 kHz. The amplitude and phase characteristics

that by changing the gain level in the controller path the characteristic |H(j@)| can very closely approximate | C(j@) | such that relation 55 is satisfied over a broad fre-

in an increase in the cancellation of the primary noise for frequencies below 300 Hz. However, as Fig. 24 implies, this will deteriorate the performance of the DFC for fre-

Using the DFC the performance of the system, as shown in Fig. 30b, is significantly improved. Cancellation

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Multichannel FSLMS algorithm based active headrest

by Ben Cazzolato

2016, 2013 International Conference on Advanced Electronic Systems (ICAES)

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Nonlinear active noise control with virtual sensing technique

by Ben Cazzolato

2016, Proceedings of the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010

In this paper a new active noise control algorithm is proposed which is suitable for nonlinear acoustical paths when using the virtual sensing technique. Conventional nonlinear ANC algorithms are suitable only for noise control at the... more

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A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

by Ben Cazzolato

2016, The Journal of the Acoustical Society of America

In active noise control (ANC) systems virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear active noise control algorithms, such as the filtered-X LMS... more

This virtual error signal is used to update the ANC weights vector w(n) according to

by filtering the reference signal x(m) with the secondary path estimate to the virtual microphone

FIG, 2. Block diagram of FSLMS algorithm using virtual sensing technique

FIG. 3. Chaotic behaviour of various signals

FIG. 7 Power spectrum magnitude (dB) of the virtual microphone signal under FSLMS based ANC for various filter order N. Duct is open at one end. 44 = 0.001, 42, =0.0001. (a) full spectrum (b) zoomed spectrum.

FIG. 8. Power spectrum magnitude (dB) of the virtual microphone signal under FSLMS based ANC for various different step-size. Duct is open at one end. Filter length N=100, 4 =0.001 (a) full spectrum (b) zoomed spectrum. the linear part. In further experiments, the step-size is fixed at 0.001 to evaluate the AN¢

FIG. 9. Power spectrum magnitude (dB) of the virtual microphone signal under FSLMS and FXLMS based ANC by artificially delaying the secondary path. Duct is open at one end. Filter length N=100, ~ =0.001, ND NNN Ln \ Lol] nee oe he ee. (LAN ere ese ee ME canes praca hccnmeeneemumie -apable of cancelling noise at the virtual location under very high causality constraint, where as source given by Eqn. (14). In this figure, it is clearly noticeable that the FSLMS algorithm i:

FIG. 10. Power spectrum magnitude (dB) of the virtual microphone signal under FSLMS and FXLMS based ANC with and without delay in the secondary. Filter length N =100, 4 =0.001, 42, =0.001 (a) Full spectrum (b) zoomed spectrum. FIG. 10. Power spectrum magnitude (dB) of the virtual microphone signal under FSLMS and FXLMS to predict the noise due to the time delay.

FIG. 11. Airfoil attached to the wind tunnel in the anechoic chamber algorithms for controlling noise generated due to airfoils such as aeroplane wings, automobile

FIG. 12. Power spectrum magnitude (dB) of the virtual microphone signal under the FSLMS and FXLMS based ANC Filter length N =100, 42 =0.001, 44, =0.001 (a) Full spectrum (b) mid-frequency zoomed spectrum (c) low-frequency zoomed spectrum. FIG. 12. Power spectrum magnitude (dB) of the virtual microphone signal under the FSLMS and FXKLMS

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Nonlinear active noise control for headrest using virtual microphone control

by Ben Cazzolato

2016, Control Engineering Practice

In this paper, a feed-forward nonlinear active noise control algorithm is developed using the Filtered-s LMS (FSLMS) algorithm and virtual microphone control for an active headrest application. Virtual microphone control is implemented to... more

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Performance evaluation of an active headrest using the remote microphone technique

by Ben Cazzolato

2016

Active headrests produce a quiet zone near the occupant's head using active noise control. It has been shown that active headrest algorithms using virtual microphones are better than those using physical microphones, as they have the... more

Figure 1 Remote microphone technique for estimating virtual error signal

H a. Estimated primary transfer function between right physical and right virtual microphone,

Figure 3 Active headrest algorithm controlling noise at physical microphones. The left and right primary reference signals are calculated according to

The conventional ANC headrest system uses the algorithm that is depicted in Fig. 3. This algorithm is designed to mini- mize the noise at the physically placed error microphones. Conventionally, these error microphones cannot be placed very close to the ear to allow head movement. Cancellation of noise at the physical microphones placed at a distance from the ears does not guarantee noise cancellation at the ear. Figure 4 Active headrest algorithm using virtual microphone arrangement. right sides, H, and Hp are estimated.

Figure 7 The power spectrum magnitude of the microphone signal placed at the left ear of the manikin.

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Active noise control at a moving location using virtual sensing

by Ben Cazzolato

2016

A problem limiting the scope of practical local active noise control applications is that the zone of quiet created at the error sensor tends to be very small. This means that the error sensor generally needs to be positioned close to the... more

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A Review of Virtual Sensing Algorithms for Active Noise Control

by Ben Cazzolato

2016, Algorithms

Traditional local active noise control systems minimise the measured acoustic pressure to generate a zone of quiet at the physical error sensor location. The resulting zone of quiet is generally limited in size and this requires the... more

Figure 2. Block diagram of the virtual microphone arrangement. iii ee eae ON ie iy A ae le The performance of a local active headrest system implementing the virtual microphone arrangement 1s been experimentally investigated by a number of authors [18, 19, 22, 23, 25, 27, 32]. An example of local active headrest system is shown in Fig. 3. Garcia-Bonito et al. [18, 19] investigated the perfor- ance of a local active headrest system in minimising a tonal primary disturbance at virtual microphones cated 2 cm from the ears of a manikin and 10 cm from the physical microphones. Below 500 Hz, the ) dB zone of quiet generated at the virtual microphone extends approximately 8 cm forward and 1C n side to side. At higher frequencies however, the assumption relating to the similarity of the primary eld at the physical and virtual microphones is no longer valid and limited attenuation is achieved at the tual location.

Figure 4. Block diagram of the remote microphone technique. finite element model of a car cabin, the tonal control achieved at a number of virtual microphones gen- erated with the remote microphone technique was equivalent to that achieved by directly minimising the measured signals at the virtual locations.

The forward difference prediction technique has several advantages over other virtual sensing algo- rithms. Firstly, the assumption of equal primary sound pressure at the physical and virtual locations does not have to be made, but also preliminary identification is not required, nor are FIR filters or similar to model the complex transfer functions between the sensors and the sources. Furthermore, this is a fixed gain prediction technique that is robust to physical system changes that may alter the complex transfer functions between the error sensors and the control sources. sents the actual pressure field and the dashed line represents the pressure estimate. The performance of forward difference prediction virtual sensors has been evaluated in a long narrow luct and in a free field, both numerica her linear or quadratic prediction tech n terms of the level of attenuation ac ly and ex hieved at perimentally, by a number of authors [40-47]. Using ei- niques, these virtual sensors outperform the physical microphones he virtual location. While the second-order estimate is heoretically more accurate than the first-order estimate, real-time feedforward experiments in a narrow luct demonstrated that quadratic pred iction tec hniques are adversely affected by short wavelength ex- raneous noise. It was also shown by Petersen [12], that the estimation problem is ill-conditioned for the hree sensor arrangement, explaining the difference between numerical and experimental results. In an attempt to improve the prediction accuracy of the forward difference algorithm, higher-ordet

where the estimated virtual secondary disturbance is given by

The active noise control system plant is described by the following state space model [8, 12] Figure 8. Block diagram of the adaptive LMS virtual microphone technique [38].

Figure 10. Block diagram of the remote moving microphone technique. Estimates, €,,(7), of the total virtual error signals at the spatially fixed virtual locations, X,, are calculated

Table 1. Summary of virtual sensing algorithms for active noise control.

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Active Noise Control at a Moving Virtual Microphone Using the SOTDF Moving Virtual Sensing Method

by Ben Cazzolato

2016, Acoustics 2009 ( …

Traditional local active noise control systems minimise the measured acoustic sound pressure to generate a zone of quiet at a physical error sensor. The resulting zone of quiet is generally limited in size and as such, placement of a... more

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Invisible speakers in home windows for simultaneous auxiliary audio playback and active noise cancellation

by Rajesh Rajamani

2016, Mechatronics

This paper focuses on an active noise cancellation system for a home window using a transparent acoustic transducer. In a traditional active noise cancellation system, direct microphone measurements are used for reference and error... more

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Nonlinear active noise control for headrest using virtual microphone control

by Benjamin Cazzolato

2016, Control Engineering Practice

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Active noise control at a moving location using virtual sensing

by Colin Hansen

2016

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Active noise control at a moving location using virtual sensing

by Colin Hansen

2016

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An approach to massive multichannel broadband feedforward active noise control using wave-domain adaptive filtering

by Sascha Spors

2015, 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Multichannel active noise control (ANC) systems are increasingly being applied in scenarios where an enlarged quiet zone is desired. For few channels numerous solutions to this problem have been developed in the past. However, algorithms... more

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Real time evaluation of soft microphones on a local active noise control system

by Stelios M . Potirakis and

2015, The Journal of the Acoustical Society of America

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Multichannel FSLMS algorithm based active headrest

by Debi Das

2015

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Performance evaluation of an active headrest using the remote microphone technique

by Debi Das

2015

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Nonlinear active noise control for headrest using virtual microphone control

by Debi Das

2015

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Adjoint nonlinear active noise control algorithm for virtual microphone

by Debi Prasad Das

2014, Mechanical Systems and Signal Processing

Controlling nonlinear noise processes at a virtual location involves higher computational complexity compared to traditional active noise control (ANC) at a physical sensor. In an attempt to reduce the computational burden, a... more

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Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

by Debi Prasad Das

2014, IEEE Transactions on Instrumentation and Measurement

In this paper, particle swarm optimization (PSO) algorithm, which is a nongradient but simple evolutionary computing-type algorithm, is proposed for developing an efficient active noise control (ANC) system. The ANC is conventionally used... more

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ADAPTIVE SIGNAL PROCESSING SYSTEMS FOR ACTIVE CONTROL OF ACOUSTIC NOISE ON CIRCULAR PATHS

by Sdiwc Conferences

2013

Figure 2: block diagram of FxLMS-based adaptive ANC The sound produced by the control source has to pass H to reach the error microphone. Similarly, the original noise has to pass G to reach this point. These two signals combine acoustically with each other across the acoustic domain. However, e(n) that is the net sound pressure at the location of the error microphone is taken as the measure of the residual noise. Consequently, an adaptive ANC system can adjust the controller W only in such a way that e(n) to be minimized.

along with the locations of the noise source (A), control source (B) are shown in Fig. 2. As seen, the control source forms a angle of ~, with the x-axis.

Figure 5: impulse response of the primary path used in the computer simulation (H) Figure 6simulation experiment 1 using original FxLMS algorithm - silent point can be only produced at the location of error microphone

Figure 4: impulse response of the primary path used in the computer simulation (G)

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Multichannel FSLMS algorithm based active headrest

by Danielle Moreau

2013

The multichannel Filtered-S LMS (FSLMS) algorithm has been used efficiently in nonlinear active noise control (ANC) because of its improved performance and low computational complexity. However, the performance of this algorithm has not... more

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Goals and problems in Active Noise and Vibration Control

by Devrukh College

2013, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-3, Issue-1

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Nonlinear active noise control for headrest using virtual microphone control

by Danielle Moreau

2013

In this paper, a feed-forward nonlinear active noise control algorithm is developed using the Filtered-S LMS (FSLMS) algorithm and virtual microphone control for an active headrest application. Virtual microphone control is implemented to... more

Fig. 2. FSLMS-based virtual active headrest algorithm tuning stage. The two block diagrams below represent details of the left and right FSLMS algorithm in the main block liagram.

Fig. 3. FSLMS based virtual Active headrest algorithm (1 x 2 x 2) at control stage. The two block diagrams below represent details of the left and right FSLMS algorithm in the main block diagram.

Fig. 4. Experimental setup for active headrest: (a) schematic diagram, (b) photograph and (c) distorted speaker. Separation of secondary speaker from ear microphone o each side is [150, 190, 220] mm.

Fig. 5. The eight secondary path transfer functions: (a) the frequency and phase (column 1 and 2), and impulse response (column 3 and 4), (b) the pole-zero plots; the corresponding secondary paths are marked at individual sub-figure.

Fig. 6. Nonlinearity models used for experimental study: (a) square nonlinearity, (b) harmonic nonlinearity, (c) distorted speaker

Fig. 7. Experimental results of Case I (square nonlinearity): (a) left and right error microphone signals with FXLMS and FSLMS based ANC algorithm and (b) the power spectrum magnitude of left error microphone signal when FSLMS based ANC is OFF and ON. The step-size for FSLMS algorithm is 0.001.

Fig. 8. Experimental results of Case II (harmonic nonlinearity): (a) left and right error microphone signals with FXLMS and FSLMS based ANC algorithm and (b) the powe! spectrum magnitude of left error microphone signal when FSLMS based ANC is OFF and ON. The step-size for FSLMS algorithm is 0.001.

Fig. 9. Experimental results of Case III (distorted speaker): (a) left and right error microphone signals with FXLMS and FSLMS based ANC algorithm, (b) the power spectrut magnitude of left error microphone signal when FSLMS and FXLMS based ANC is ON compared to the when ANC is OFF and (c) Attenuation in dB with respect to th frequency with FXLMS and FSLMS algorithm. The step-size for FSLMS algorithm is 0.001.

Fig. 10. Spatial distribution of noise level around the Left side of the head of the occupant of the headrest: (a) ANC with physical microphone control, (b) ANC with actu: ear microphone control and (c) ANC with virtual microphone control.

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Performance evaluation of an active headrest using the remote microphone technique

by Danielle Moreau

2011

Active headrests produce a quiet zone near the occupant’s head using active noise control. It has been shown that active headrest algorithms using virtual microphones are better than those using physical microphones, as they have the... more

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Nonlinear active noise control with virtual sensing technique

by Danielle Moreau

2011, Intelligent Sensors, Sensor …

Fig. 1 Comparison of local active noise control at (a) a physical sensor; and (b) a virtual sensor [3].

Fig. 3 The acoustic duct model used in the simulation

Fig 4. Schematic of Simulation Experiment in Matlab™ Simulink Fig 5. Primary path as a liner-nonlinear-linear model

Fig. 7. The last 200 samples of the signal for Wiener (Linear + Nonlinear) primary path case during the training for 30000 samples: é, : error estimate at

Fig. 9. The last 200 samples of the signal for Hammerstein (Nonlinear+Linear) primary path case during the training for 30000 samples: é, : error estimate at virtual location, d,, : estimate of desired signal at virtual location, }, : controller output signal estimate at virtual location, e, : received error signal at physical location.

Fig. 11. The last 200 samples of the signal for Linear+Nonlinear+Linear primary path case during the training for 30000 samples: e,, : error and (d) error estimate at virtual location, é, .

Fig 10. Plot of signals for Linear+Nonlinear+Linear primary

Fig 12. Plot of signals for linear primary path: (a) desired signal at virtual location, d,, , (b) output signal virtual location, 3, , (c) error signal at physical location, e, , and (d) error estimate at virtual location, é,, .

Fig. 13. The last 200 samples of the signal for linear primary path case during

COMPARISON OF NOISE ATTENUATION VALUE Atn(dB) )F FKLMS AND FSLMS BASED ALGORITHMS WITH VIRTUAL SENSING

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A review of virtual sensing algorithms for active noise control

by Danielle Moreau

2011, Algorithms

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Active noise control at a moving virtual sensor in three dimensions

by Benjamin Cazzolato

2011

A common problem in local active noise control is that the zone of quiet generated at the physical error sensor is limited in size. This requires that the physical error sensor (the microphone) is placed at the desired location of... more

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Active headrest

Key research themes

1. How do occupant seat positions and postures affect head and neck injury risks and discomfort in vehicle crashes?

2. What are the effects of seat design and suspension systems on occupant comfort, muscle fatigue, and vibration transmission during prolonged vehicle use?

3. How can active/headrest-based noise and motion control technologies enhance occupant comfort and safety in vehicles and other transport modes?

Related Topics

All papers in Active headrest