Indoor pass-by noise contribution analysis using source
path contribution concept
A. Schuhmacher1, Y. Shirahashi2, M. Hirayama3, Y. Ryu1
1
Brüel & Kjaer Sound & Vibration Measurement A/S
Skodsborgvej 307, 2850 Naerum, Denmark
e-mail: andreas.schuhmacher@bksv.com
2
Nissan Motor Co Ltd., Technical Center
560-2 Okatsukoku, Atsugi-shi, Kanagawa 243-0192, Japan
3
Brüel & Kjaer Japan
2-6 Kandatsukasa-machi, Chiyoda-ku, 101-0048 Tokyo, Japan
Abstract
This paper presents a vehicle pass-by noise analysis method based on a pure time-domain acoustic source
modelling technique. Originally this technique was developed for prediction of interior vehicle sound from
component sources but has been included in the measurement process for indoor simulated pass-by
application as described here. Each component noise source is modelled using point sources and an extra
set of indicator microphones is positioned close to the noise sources for the purpose of source separation
and identifying the operational source strength for each noise source. A set of acoustic transfer functions is
needed to accomplish the source separation and another set of transfer functions will be used to predict the
contribution from each source to the pass-by noise level. The overall predicted levels from the vehicle
model will be compared to the standard indoor simulated pass-by levels for validation.
1
Introduction
Indoor vehicle pass-by noise applications deal with measuring the exterior noise from a vehicle which is
fixed on a chassis dynamometer inside a semi-anechoic chamber. During a standardised acceleration test
the noise is measured with a line-array of microphones placed in the vehicle far-field, and the overall noise
level versus vehicle position can be determined. As such the indoor test is a simulated pass-by noise
measurement. Since the indoor facilities allow controlled and repeatable measurements independent of
weather this test is popular during development stage of vehicle design allowing modifications being
tested out in a fast manner.
In addition to performing the indoor pass-by noise test there is a growing need for vehicle noise source
contribution analysis related to this test. A source path contribution concept will be presented involving
modelling the main vehicle noise sources contributing to the pass-by noise. A special feature of this
approach is that it processes entirely in the time-domain, working on measured time recordings during the
fast acceleration test which are initially filtered to produce source strength estimates for the considered
noise sources followed by another set of synthesis filters to produce pass-by noise estimates for the vehicle
and for individual noise sources. As a result we can identify the major noise source contributions at a
given vehicle position during the pass-by test.
For testing and validating the proposed concept an indoor pass-by noise measurement was carried out for a
passenger car on a chassis dynamometer. In addition to the line-array of microphones in the far-field extra
indicator microphones were located close to the noise sources during the test. Indicator microphone data
recorded during the acceleration test and transfer function data measured using a small volume velocity
source are combined to form the vehicle source model. A further synthesis process allows predicting
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exterior pass-by noise from the vehicle source model. The predicted pass-by result from the complete
vehicle source model is compared with the indoor pass-by result measured at the standardised pass-by
distance for validation of the chosen model setup. Furthermore the individual noise component
contributions are shown.
2
Indoor simulated pass-by noise measurement
A pass-by noise measurement is defined as the method of measuring the noise emission of a road vehicle
under acceleration conditions, with various gear positions in a certain measurement range. These
measurements are mandatory for automotive manufacturers in terms of product certification. For this
reason, ISO (International Organization for Standardization) regulates the measurement and analysis
procedures, as well as the reporting format [1].
Pass-by noise measurements are the most important part of troubleshooting in the mass production of cars,
for example in the analysis of engine, intake, exhaust, and tyre noise. The pass-by noise measurement
method is designed to meet the requirements of simplicity, as far as it is consistent with the reproducibility
of the results.
The pass-by noise measurement should be performed in a large open space, for type approval of
commercial vehicles, or measured during the official test station’s manufacturing stage. Therefore, it is
very important that the certification of emission noise measurements is performed before mass production
starts.
In some cases, however, pass-by noise measurements cannot be taken out in ‘the field’ because of bad
weather or poor test-track conditions. In such cases, the indoor simulated pass-by noise measurement is
often used. The indoor simulated pass-by noise measurement does offer a number of advantages such as
good repeatability, flexibility, ease of use and is being considered as the conformance test, together with
the field pass-by test by ISO standard.
An outdoor/field pass-by measurement would experience the Doppler effect, so to make a better
simulation of outdoor measurements, the indoor pass-by system can introduce a correction that simulates
the Doppler effect.
Figure 1: Field pass-by and indoor simulated pass-by noise measurement.
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Instead of making the test vehicle pass two stationary microphones as being the standard in a field pass-by
measurement, indoor pass-by setups use two rows of microphones placed alongside the vehicle, see figure
1 for a comparison of the two different situations. The vehicle runs on a chassis dynamometer (dyno) and
is accelerated in the same way it would be for a field pass-by measurement. Time histories are measured
by the microphones in conjunction with vehicle parameters and dyno drum speed. A sophisticated
algorithm uses information from the dyno to calculate the vehicle’s position relative to the microphones as
a function of time. This is then used to extract the contributing portions of the time histories that
correspond to when the vehicle would have passed the standard microphone positions had it been moving.
A synchronised single time history is created by stitching all of these time history sections together and
interpolating across the segments’ boundaries. This synchronised single time record combined with the
dyno drum speed profile represents the vehicle noise emitted during a pass-by measurement. This new
time history is then played back through the analysis section of the system, offering the option of applying
various types of frequency analysis to the time history. It can also be previewed and listened to in order to
determine whether it sounds right. Figure 2 illustrates the process of calculating the vehicle’s emitted passby noise level as a function of position from a set of time recordings and the chosen synchronisation
condition – here 50 km/h at 0 m (line PP).
Figure 2: Indoor pass-by simulation approach based on synchronised time recordings.
The indoor pass-by system described in this paper has been developed to allow for microphone positions
closer than 7.5 m from the vehicle while still providing correct results. This is extremely useful for
situations where space is limited and is achieved assuming that the noise is emitted from one point (an
acoustic centre) as seen from the far-field. Individual acoustic centres can be chosen for the left and the
right side of the vehicle.
3
Airborne source path contribution model
In order to view contribution levels of different vehicle noise components, a more sophisticated approach
is needed based on separating noise sources and predicting each sources’ contribution to the pass-by
microphone positions. Experimental modelling methods based on acoustic transmission path analysis, or
SPC (Source Path Contribution) are typically required wherein a certain number of source positions are
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defined close to the true acoustic sources [2, 3]. As such each relevant noise component of the vehicle, e.g.
engine, intake and tyre is modelled via a set of assumed point sources covering a component and being
able to represent the radiated sound field. Next step is to quantify the source strength of each modelled
point sources and finally we can predict the noise at a certain position from one component only. The
modelling principle is sometimes termed source substitution method.
As an example consider the complex noise source encapsulated by some structure in figure 3 resembling
the situation of an engine inside engine compartment. The noise generated by the vibrating surface is
modelled using a set of distributed point sources and each source’s operational strength is estimated using
a set of indicator microphones preferably located close to the point sources. To accomplish this acoustic
transfer functions are measured between all point sources and all indicator microphone positions to allow
full matrix coupling between all sources and indicators. A calibrated volume velocity source is used for
this task. If the model is used to predict the operational response at a receiver location we can measure this
acoustic transfer function at the same time. Then the actual noise source is turned on and operational data
is recorded for all the indicator microphones, and if possible also at a desired receiver microphone as
illustrated. Since the operational data and transfer function data are both acquired within the same
environment they fit together and we can firstly quantify the operational source strength for each point
source given the matrix of transfer functions and the indicator operational data. Here the matrix of transfer
functions is usually provided as a set of Frequency Response Function’s (FRF’s) measured by the volume
velocity source as sound pressure over volume velocity (p/Q) FRF’s.
Figure 3: Source substitution method applied to a complex vibrating geometry encapsulated by another
structure.
Since all indicators microphones receive contributions from all noise components – more or less, we have
to set up the relation between source strengths and indicator sound pressures as a matrix formulation:
(1)
where H is the matrix of source-to-indicator FRFs, q is the vector of source strength and p is the vector of
measured indicator sound pressures. In principle we can then invert H and multiply with p to obtain a set
of source strengths that fit our measured indicator pressures via our measured source-to-indicator FRFs. In
many situations H will have a rectangular form since more indicator microphones than source positions
are chosen so in that case we will solve equation (1) in a least squares sense. This equation is usually
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solved in the frequency domain, however since our operating data exists as time recordings of fast runup’s, we prefer to solve the matrix problem directly in the time-domain.
One problem common for all matrix solvers has to be dealt with, the possibility of having an illconditioned and thus unstable FRF matrix. Stability in terms of regularisation can be introduced if some
small terms are removed from the FRF matrix, however if too much information is removed the estimated
source strengths become to “rough”. A singular value decomposition (SVD) routine is run for every
frequency line of the FRF matrix, the set of singular values will be grouped into two groups: 1) retained
set and 2) discarded set of singular values, where the retained set will consist of some amount of the
largest singular values since they account for most of the information in the FRF matrix. Since the SVD
routine returns singular values magnitude sorted we have to select a threshold dividing the set into the two
groups. The chosen threshold is relative to the largest singular value for every frequency line, that is a
threshold of 1/10 means all singular values from the largest down to 0.1 times the largest will be put into
the group of retained singular values, the rest below will be discarded. As such the group of discarded
singular values can be considered as representing noise and other errors in the FRF matrix. A default
threshold value is normally used for every frequency line however one can fine tune this value if preknowledge about the true error levels of the FRF matrix and also the indicator operating data is available.
In the following approach we will solve for the estimated operational source strength’s using a timedomain procedure where the inverse of the FRF matrix is turned into a matrix of inverse filters which are
used to perform multi-channel inverse filtering on the operational indicator time data recorded during the
operating noise source [4]. We can write this operation as:
() ℎ() ∗ ()
(2)
where [h] represents the matrix of finite impulse response (FIR) inverse filters based on the inverse of
measured FRF’s. Here care must be taken when inverting the FRF matrix since it is prone to illconditioning and regularization techniques would be necessary. The convolution operator ‘∗’ is introduced
at this stage.
The set of operational source strength’s for the complex noise source constitutes a source descriptor and
given another set of transfer functions from the same point source positions to a receiver microphone
position we can predict the noise generated at the receiver from the complex noise source by adding up the
contributions from each source position:
() ∑ () ∗ ()
(3)
where g is now a forward FIR filter between source and receiver. The delays introduced by the filters in h
and g can be removed so that the resulting predicted time data will be synchronized with the original
measured dataset. If we had measured the noise at the receiver during source operation we can in fact
validate the result from the prediction and assess the quality of the noise source modelling.
4
Vehicle exterior noise modelling
The airborne SPC approach presented for the individual component case can be extended to a complete
vehicle modelling all major noise sources using a set of point sources for each component whose source
strength during pass-by operation is estimated indirectly with a set of indicator microphones close to each
component. The source positions are preferred to be located close to the noise mechanisms like vibrating
surfaces of engine, tyre and muffler or at the orifices related to intake or exhaust. Although the indicators
can be placed in a flexible manner it is good advice to place at least one indicator close to one source
position while still capturing the full radiated noise from the component and extra indicators will make
sure that all radiated noise is taken into account and at the same time improve the conditioning of the
source separation step. A typical near-field setup of indicator microphones is shown in figure 4 together
with a distribution of source positions for some vehicle noise components. The transfer functions required
for linking sources and indicators and also source and pass-by receivers can be measured simultaneously
for each source position by placing the volume velocity source directly at a selected source position.
Alternatively a reciprocal procedure can be chosen where the volume velocity source is placed at each
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indicator and pass-by receiver position and extra microphones would be attached at all source positions
making it a rather cumbersome procedure. The direct FRF measurement procedure would be preferred
thus sometimes requiring enough space around each source position for the volume velocity source to be
positioned correctly.
Figure 4: Typical layout of source and indicator positions around main vehicle noise components for
indoor pass-by noise contribution analysis.
Having completed operational pass-by tests with the vehicle standing on the chassis dynamometer and
also measured all acoustic transfer functions between any source position and any microphone position we
can setup one global or several smaller matrix problems for estimating all operating source strengths over
the whole vehicle. By dividing into smaller matrix problems we can eliminate the cross-coupling terms
between noise components which are known to be negligible and thus improve the source separation
among the component noise sources. Another possibility showing the flexibility of the modelling
procedure could be to define one vehicle source model representing only the noise radiated to the vehicle
left side and similarly one for the right side.
The output from the first step described in the previous section is a set of source strengths (as time data)
representing the actual vehicle noise components. We assume that the different noise components have
been sufficiently separated, which is all dependent upon the quality of the noise component modelling, the
positioning and amount of indicator microphones, the matrix regularisation etc.
The second step makes use of another set of transfer functions, from all source positions to one or several
receiver positions. Using the obtained vehicle model from the first step above in combination with the
second set of transfer functions provides estimates of the sound field at all the receivers. Here the receivers
will be the microphones of the pass-by array. For each receiver we can write the contribution from one
source position or make simple time series summation in order to express the predicted contribution from
a group of sources, which is necessary to get the contribution from a noise component modelled by several
point sources or to get the overall vehicle contribution, i.e. sum over all source positions.
After removing any delays related to time filtering the estimated source strengths will be synchronised
with the indicator microphone recordings used as input, and further synchronised with other measurement
data like pass-by array microphones and tachometer data measured simultaneously with the indicators.
From here we can either use the measured or the predicted time data at the pass-by microphones run them
through the indoor simulated pass-by algorithm and obtain the test result in terms of pass-by noise levels
versus vehicle position. Individual noise source contributions are processed in a similar manner.
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5.1
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Experimental case study
Measurement setup in chamber
A passenger vehicle was put on a chassis-dynamometer inside a large semi-anechoic chamber which
accommodates arrays of microphones both to left and the right at 7.5 m distance. The vehicle was
equipped with standard tyres having a tread pattern meaning significant tyre noise is generated from the
two front tyres which were the only rolling tyres for this setup. Two pass-by arrays of 7 microphones each
were placed to the left and right of the vehicle. The noise sources considered for this setup were
engine/transmission, intake, front right tyre, front left tyre and two exhaust orifice as sketched in figure 5
together with an indication of the number of point sources considered for the whole vehicle. In the present
study a somewhat different configuration of indicator microphones was considered especially for engine
compartment noise sources. Line-arrays of microphones were placed close to the floor in front of the
vehicle and other arrays were located behind the front wheels again close to the ground to pick up the
noise radiated from the engine/transmission. Other noise sources were measured using close indicator
microphones. The reason for having the engine/transmission indicators outside the engine room was for
practical investigation purposes, simply seeing if it were possible to achieve sufficient noise source
separation with such a setup. All in all 24 source positions were considered and 34 indicator microphones
for the full setup.
Figure 5: Indoor pass-by noise contribution analysis setup considered for case study.
5.2
Measurement process
Having decided how to model the major noise sources for the given vehicle and prepared the necessary
layout of indicator microphones we can proceed with the actual measurements needed to fulfil the task of
contribution analysis related to exterior pass-by noise. Basically two separate measurement tasks must be
completed to do the full analysis: firstly, for each pass-by condition time recordings for all involved
microphones and tachometer pulses must be generated and secondly a FRF model between all point
sources and all indicator/pass-by receiver microphones must be measured using a volume velocity source.
For the operating tests synchronized time recordings are streamed to disk for later processing based on
type of pass-by test etc. Different operating conditions were considered and different runs were made for
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each condition, the main condition being D-range mode for the chosen vehicle since it was equipped with
an automatic CVT transmission. The frequency range up to 6.4 kHz is considered for these measurements.
Following the operating tests a series of acoustic transfer function measurements were carried out
considering a reciprocal procedure where the nozzle opening of a hose-based volume velocity source was
positioned at each indicator microphone in turn while having an extra set of microphones placed at all the
assumed source positions. Additionally all pass-by receiver microphones were measured in a similar
manner. The sound source for these transfer function measurements is shown in figure 6, an OmniSource
B&K Type 4295 equipped with a long flexible hose and a two-microphone volume velocity adaptor close
to the hose opening [5]. The operating frequency range is from around 50 Hz up to 6 kHz with a full
omnidirectional behaviour in the main frequency range below 3 kHz. Random noise excitation signal and
averaging was used for generating all the FRF’s between sources and microphones. Each measured FRF is
labelled with two scalar DOF (degree of freedom) numbers, one for the input source point and one for the
response location. The labelling scheme can be identified from figure 5, where each source and
microphone has a unique label. The purpose of the labelling procedure is to simplify the model setup and
calculation procedure which happens more or less automatic.
Figure 6: Volume velocity source used for acoustic transfer function measurements.
5.3
Indoor Pass-by Noise Analysis
Firstly all considered sources and indicator microphones are used to form one global matrix problem for
identifying all operational source strength’s in one step taking any cross-talk into account, i.e. a FRF
matrix of size 32 indicators x 24 sources is established for the inversion step and hence later the source
separation stage. For this special arrangement of indicators where the engine and intake are not closely
covered we will expect some difficulties when inverting the matrix, a relatively high condition number being at least 50 - will be experienced as revealed by Figure 7 where the structure of matrix at the
frequency line of 3 kHz is shown as well. For the lower frequencies below 1 kHz the condition number
increases dramatically due to the microphone arrangement and regularisation of the matrix is definitely
needed in this situation otherwise overestimated source strength’s will be obtained leading to
overestimated pass-by noise contributions. In case we use a threshold of 1/10 as explained previously the
regularised FRF matrix will have a smaller condition number equal to 10 for every frequency line making
the calculation of source strength’s less sensitive to any noise in the available FRF and operating data.
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Figure 7: Condition number of source-indicator matrix for each frequency line and colour plot of matrix
structure at 3 kHz.
Now having a set of source strength time data which are synchronised with the measured D-range
operating dataset we can filter those with the time filters between source positions and any pass-by array
microphone to obtain the predicted sounds from any component, again synchronized with the original
measured dataset since any source-receiver delay is taken into account by the filters. As a result we have
the predicted overall sound from the vehicle model plus individual sounds from component noise sources
at all pass-by array microphones synchronized with the measured recordings. From there on the rest is
based on standard indoor simulated pass-by calculations where the time recordings are synchronized with
the pass-by test condition being 50 km/h at vehicle position 0 m which corresponds to the line PP for a
field pass-by measurement. This condition corresponds to the new ISO-362 pass-by standard.
The final contribution analysis plot for vehicle left side is shown below in figure 8 in terms of A-weighted
overall SPL versus vehicle position on the simulated track. Notice that the result is shown from vehicle
position -10 m to +10 m although the pass-by arrays only covered the vehicle positions from roughly -2 m
to +10 m, so the extrapolation capability of the indoor pass-by calculations has been used. Here the red
line represents the standard indoor simulated result based on measured data from the left pass-by array
microphones and the blue line is the same calculation based on predicted sounds form the overall vehicle
SPC model (sum of all contributions), hence an adequate model should show similarity between those two
lines. Individual component contributions are also provided showing that the engine is the main
contributor followed by the tyres for this vehicle setup, also we see the exhaust becoming more important
towards the end of the pass-by test. Extra information can be gained by investigating the third octave
levels for a given vehicle position, two example plots are considered for vehicle position 0 m and +8 m,
respectively. In most bands the agreement between the measured indoor pass-by result and the overall
vehicle model prediction is excellent. Again we see the engine being the dominant source with
contribution from the tyres in the 1/3 octave bands above 1 kHz.
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Figure 8: Indoor simulated pass-by test result (Overall SPL(A) vs vehicle position) including contribution
analysis for D-range pass-by acceleration test condition. 1/3 octave contribution levels are shown for
vehicle position 0 m (lower left) and +8 m (lower right).
Constant vehicle speed data for 50 km/h were also recorded and streamed through the same vehicle SPC
model as described for the D-range acceleration test. A similar trend is seen for this condition, the engine
being the main contributor followed by the tyres whereas the exhaust orifice is a minor contributor as
expected. Again the agreement between indoor simulated pass-by test result and overall vehicle model
prediction is excellent.
Figure 9: Indoor simulated pass-by test result (Overall SPL(A) vs vehicle position) including contribution
analysis for pass-by 50 km/h constant speed test condition. 1/3 octave contribution levels are shown in the
lower plot for vehicle position 0 m.
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Conclusion
In this paper we have been dealing with indoor vehicle pass-by noise measurements introducing a
modelling procedure and time-domain processing for quantifying the main noise sources during operation.
The result from the modelling procedure is pass-by noise contribution levels for each component noise
source as well as overall vehicle levels which should be comparable to the standard indoor pass-by test. A
test case revealed the similarity between the modelled and measured pass-by levels for the overall vehicle
as well as indicating the contribution from the main noise sources during the pass-by. Since the described
approach is considered to be a synthesis prediction the overall pass-by levels from the vehicle model can
be validated against the standard indoor measurement thus making it possible to check whether all noise
sources are taken into account and sufficiently modelled. The overall vehicle model verification does
however not clarify if the source separation was successful though the results presented in this paper
indicate realistic contribution levels as well. Since the result from the indoor simulation pass-by algorithm
is a simulated time history all sorts of post-processing can be done, e.g. listening tests or analysing 1/3
octave spectra at a specified vehicle position.
References
[1] ISO 362, Acoustics – Measurement of noise emitted by accelerating road vehicles – Engineering
method, International Organization for Standardization (ISO).
[2] J. W. Verheij, Inverse and reciprocity methods for machinery noise source characterization and
sound path quantification. Part 1: Sources, Int. Journal of Acoustics and Vibration, Vol. 2, No.1,
(1997), pp. 11-20.
[3] J. W. Verheij, Inverse and reciprocity methods for machinery noise source characterization and
sound path quantification. Part 2: Transmission Paths, Int. Journal of Acoustics and Vibration, Vol.
2, No.3, (1997), pp. 103-112.
[4] A. Schuhmacher, D. Tcherniak, Engine contribution analysis using a noise and vibration simulator,
Proceedings of the 2006 International Conference on Modal Analysis Noise and Vibration
Engineering (ISMA), Leuven (2006), pp. 2641-2648.
[5] A. Schuhmacher, Investigation of volume velocity source based on two-microphone method for
measuring vibro-acoustic transfer functions, Proceedings of the 2008 International Conference on
Modal Analysis Noise and Vibration Engineering (ISMA), Leuven (2008), pp. 3697-3709.
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