Download - Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 1 Susham Biswas & Bharat Lohani Geoinformatics Laboratory Indian Institute of Technology

Indian Institute of Technology KanpurIndian Institute of Technology Kanpur

Susham BiswasSusham Biswas

11

Susham Biswas & Bharat Lohani

Geoinformatics LaboratoryIndian Institute of Technology KanpurKanpur, 208016 INDIA20th January , 2011

Sound Propagation Modeling at High Sound Propagation Modeling at High Resolution Using LiDAR Data and Aerial Resolution Using LiDAR Data and Aerial Photograph for Outdoor environmentsPhotograph for Outdoor environments



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Noise mapAround Airport

Impulsive sound propagation i.e., gun shot, bomb blast etc

Urban Noise Map

(e.g., UK Noise Map)

Animation

Videography-movie

Representation of Outdoor Sounds and/or its

applications



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Outdoor Sounds and its Relationship with Spatial and other Data : Modeling aspect



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Sound Modeling

Sound Model

Sound Input

Spatial Parameter

Sound Output

Sound modeling



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Outdoor Sound Propagation

Diffracted Wave

Ground Reflected Wave Ground

Sound

Transmitted Wave



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Sound ModelingNumerical Modeling

•Classical Wave Equation Solutions•Approximate Wave Equation Solutions•Diffracted Sound Field Determination Techniques•Boundary Element Method

Empirical or Semi-Empirical Modeling –It has the following components

•Directivity•Distance Attenuation•Atmospheric Attenuation•Ground Attenuation•Barrier Attenuation•Barrier_ground attenuation•Meteorological Correction•Vegetative Attenuation•Reflection Correction



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Sound Modeling (Semi_emperical)

How sound can reach receiver R from source PS

Direct Transmission

Direct transmission of sound from source to receiver



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Ground Reflected

Sound transmission after ground reflection




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Diffracted over and around sides of building




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Wall reflected

Transmission through reflection from wall




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Tree absorbedHow sound can reach receiver R from source PS



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In outdoor environment sound from a source follows one or many of the above paths before reaching to a receiver location thus the outdoor Sound propagation involves following spatial parameters

1. Distance between Source* (PS) and Receiver (R)

2. Path_difference for Diffraction3. Path for Ground_reflection with ground type4. Possibility of Wall_reflection5. Extent of path length involved in

tree_absorption

* When there exists objects between Source and receiver the intermediate diffracting, reflecting points involved in transmission is termed as secondary source (SS) and original source as primary source (PS)

Spatial information required in sound modeling !!



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11)log(20or )4log(10 2 dBddBdA SRSRD

2/2/

_

ddifferencePath

N

Sound model (Important components)

ydirectivitreflectionAAAAARSPLSSPL AtmTreeGBD )()(

32 2872446665.5 NNNAB

Where N: Fresnel Number

Distance attenuation Building attenuation

Poor Sound Model

SRfAtm daA

mdBKHzfKHzfaOctavef /10])(036.0)(36.002.0[ 22

)(

Atmospheric attenuation

Spatial Parameter



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Poor Sound Model

Sound model (Important components)

Spatial Parameter

MRSG AAAA

Ground attenuation

3 Zone approach (ISO-9613-2)

Complex approach

Ground reflecting point dependent

Tree attenuation

erceptiontreeft dtA int_

mdBHzft f /]))((006.0[ 3/1



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Existing scenario: In Spatial Data and its Input Techniques-Weaknesses

Spatial Input using• Approximate Techniques (e.g Spot Heights)• Contour • Vector based Approach (e.g., Map Digitization)• Accurate Comprehensive Surveying (time limitation)

Existing practices

Spatial Data• Approximate estimation of terrain heights• Low resolution satellite image/ aerial photo• Use of Total Station/GPS in limited scale



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Objectives of research

1. Can Lidar data be used for sound modeling- how?

2. Is there any Advantage of using high resolution spatial data?

3. Whether Better Data and/ or Model can lead to better prediction?



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Observed Sound

at outdoor

Prediction of Sound at Outdoor with Poor Data

and Poor Model

Prediction of Sound at Outdoor with Good Data

and Poor Model

Prediction of Sound at Outdoor with Good Data

and Good Model

Comparison

Sound Prediction Schemes

Field Measurement

Is there any Advantage of using high resolution spatial data? Whether Better Data and/ or Model can lead to better prediction? Can better data suggest ways of improvement in sound modeling?

Can LiDAR data & Aerial Photograph be used for

Sound Modeling?

Methodology



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Methodology

Field Measurement

Sound MeasurementSpatial data MeasurementNon_Spatial data Measurement

Sound Prediction Schemes

Poor Data and Poor ModelGood Data and Poor ModelGood Data and Good Model

Comparative Analysis



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Sound Model

Spatial Parameters

Non-Spatial Parameters

Spatial Survey

Measured Field Data

Design of Model

Design of Algorithms

PredictedSound for outdoor

location

Sound Prediction Scheme



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Design of Algorithm to extract Spatial ParametersData Preparation

For Good data: Accurate Data e.g. from Total Station, or LiDAR and Aerial Photographic Survey etc

For Poor data: Errors added to good data



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LiDAR data points

Modeling parameters

Source sound level

Sound Model Receiver sound level

ClassificationTriangulation / cluster formation/Other

Cutting plane technique

Ground points

Building points

Tree points

Building corner points and building

edges

Ground TIN TIN from tree points cluster

Determination of principal

propagation paths

Coordinates of source and

receiver

Ground type

attribute

Classified aerial photo

How to do



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Aim

Determination of all principal paths for

propagation



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Determination of Principal Path due to diffraction (over top)



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Cutting plane orthogonal to X-Y plane and passing through source and receiver



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Intersecting points

Intersecting points with vertical line of intersection between buildings and cutting plane

Intersecting points & vertical line of intersection



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Finding effective intersecting points



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Determined of Principal Path due to diffraction (over top)

Determined principal path over top (i.e., PS-c-d-R)



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Determination of Principal Path due to diffraction (around sides)




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Intersecting points at cutting plane



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Intersecting points and line of intersection between building side walls and cutting plane

Intersecting points & line of intersection



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Intersecting points and lines of intersection projected on XY plane

Intersection points with projected lines of intersection



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Principal paths around sides (i.e., PS-a-e-h-R and PS-b-c-R)

a

b c

d

e

f

g

h



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Different Routes in complex Scenario



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1

2

3

4

5 & 6

7 & 8

Eight Principal path or routes between any pair of Source and Receiver

Source

Receiver

Source

Receiver

Source

Receiver

Source

Receiver

Source

Receiver

Source

Receiver



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Determination of Principal Path for ground reflection

Classified ground point

Triangulation Comparison of TIN planes with PS-R vertical plane



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Determination of Principal Path stretch causing tree absorptionClassified tree points

K-Mean clustering Triangulatio

n

Finding TIN planes intersected by PS-R line



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Source

Receiver

Reflecting Point

The plane of “Source – Reflecting Point – Receiver” The plane of “Wall”

Finding the point of reflection (if any)



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Dist S-RPath-DiffAvg. Gr.Type

Spatial Data

Prediction Scheme with Poor Model

- AD AB AG+ +SPLS( ) =

Predic ted SPL

AAtm+



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Prediction Scheme with Good Model1

.

.-SPLS =

Predicted SPL

• Dist S-R• Path-Diff w.r.t. 8 Principal

routes• Gr. Type of ground reflecting

point• Angles of reflections

• Phase change at reflection• Shape of Barrier

• Turbulency Factors

Spatial Data

1AD1ABG

1AAtm+ +1DI + 1AR+ 1Aveg.+

ABG

..

8AD8ABG


Coherent

AD

..AR

..Aveg.

..



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Prediction Scheme with Good Model2

.

.-SPLS =

Predicted SPL

Dist S-RPath-Diff w.r.t. 8 Principal routesGr. Type of ground reflecting point

Angles of reflectionsPhase change at reflection

Shape of BarrierTurbulency Factors

1AD1ABG


ABG

..

8AD8ABG


Incoherent

AD

..AR

..Aveg.

..

Spatial Data



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Design of Experiment

Experimental SetupsSound GenerationSound MeasurementPosition MeasurementExperimental Parameters

Frequency of soundSPL/Leq

Experimental ScheduleExperiment

Primary ExperimentAuxiliary Experiment



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A transact (130mX 30 m) at IIT Kanpur Air strip containing building, different ground types, tree, is used to monitor SPL and validate that with developed model

Validation experiment



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Measured Sound at Receiver

Propagated Sound

Spatial Survey

Measured Sound at

SourceTotal Station

Reflector for TS

Experiment-Measurement of Sound and Spatial data



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Computer

(Generation of Tonal

Sound)

AmplifierSpeaker(output device)

Experimental Detail- Sound Generation



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Experi-mental Site

Parameter studied

Frequency (Hz) of Study

No. of Position

No. of Ht/position

Total No. Observation

Duration of measurement at each observ-ation

Metero-logical Condition

130mX 30m, At Air Strip, having building and different ground types

SPL 250, 500,1000,4000

70 3(0.7m,1.35m,1.75m)

846 90 sec Near Neutral

Experimental Detail- Sound Measurement (at receiver)

Sound Measured simultaneously at a fixed position at source as well



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Measured SPL in dB

dBFrequency=250Hz



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dBFrequency=250Hz

Predicted SPL in dB (for Good data and Good Model1)



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Good data – Good Model1 -250 Hz

Deviation

in dB

Deviation between measured & predicted



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Good data – Good Model1 500 Hz

Deviation

in dB




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Deviation

in dB




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Deviation

in dB




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Interdata analysis for three different schemes of prediction

•Mean and SD•ANOVA•Paired t Test•Tukey Test

Error Propagation

Intradata analysis for the best prediction scheme of the above three

Data Processing and Data Analysis



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Mean STD Max MinFreq

250 7.12 6.55 39.87 0.01Good Data & Good Model 1








250 11.99 9.45 52.73 0.15Good Data & Poor Model 500 16.64 12.42 51.62 0.02Good Data & Poor Model 1000 10.59 9.09 47.49 0.11Good Data & Poor Model 4000 10.16 9.49 41.99 0.10Good Data & Poor Model

250 12.86 9.65 52.24 0.15Poor Data & Poor Model 500 16.85 12.80 51.29 0.04Poor Data & Poor Model 1000 11.14 9.53 47.01 0.06Poor Data & Poor Model 4000 10.42 9.33 41.51 0.07Poor Data & Poor Model

Comparison of Different prediction SchemesStatistical Analysis



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Frequ-ency

Probability of H0 to be true at 0.05 significance level

250 Hz 1.11*10-16

500 Hz 0

1000 Hz

0.0018

4000 Hz

0.5354

250 Hz

500 Hz 1000 Hz 4000 Hz

ANOVA statistical test for four prediction schemes

At least one of the four prediction schemes giving different results

Statistical Analysis

Abbreviations usedM1=Good data & Model1M2=Good Data & Model2GP=Good Data & Poor ModelPP= Poor Data & Poor Model

Comparison of Deviation

µ(D)M1=H0: µ(D)M2=µ(D)GP= µ(D)PP



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Freq µ(D)M1-M2 µ(D)M1-GP µ(D)M1-PP µ(D)M2-GP µ(D)M2-PP µ(D)GP-PP

250 3.08 10.98 12.31 10.91 14.49 2.66500 3.49 14.38 13.74 14.62 15.95 0.54

1000 3.28 7.99 7.97 4.82 8.04 1.394000 4.42 2.16 2.46 1.54 1.50 0.51

All 7.19 17.56 18.03 13.58 17.59 2.38

Paired ‘t’ Test

Abbreviations usedM1=Good data & Model1M2=Good Data & Model2GP=Good Data & Poor ModelPP= Poor Data & Poor Model

Statistical Analysis

Comparison of different pairs of prediction schemes in terms of deviation

t(0.05,120)=1.645

H0: µ(D)1= µ(D)2

Generally the pairs of prediction schemes are not giving similar results



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250 8.31500 8.311000 9.674000 10.98

Error in DeviationFreq

.

Propagation of Error

Data analysis



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Hence, generally the prediction scheme involving Good Data & Good Model1(Coherent) seems to performed the best

Analysis



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Study area: part of academic area of IIT Kanpur. Buildings are shown with red and ground with blue

SPL (in dB) at different locations due to distance attenuations

SPL (in dB) at different locations due to ground attenuations

Stages of sound prediction over LiDAR data points



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SPL (in dB) at different locations due to barrier attenuations

SPL (in dB) at different locations due to distance + barrier attenuations

Binary plot of probable reflecting and non-reflecting points

Stages of sound prediction over LiDAR data points



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Sound propagating from a single source to nearby points in 3D

Sound map developed by incorporating LiDAR data/Google Earth Image of IIT Kanpur inside sound model

Representation of Sound

250 Hz sound of 90 dB propagated



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Perception of a street noise at different spatial location

Audio Realization

G T Road

IIT Gate



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Discussion and Conclusion

• LiDAR/Aerial Photo data can be use to incorporate detail terrain information for outdoor

sound propagation modeling • In general Good data and Good Modeling (complex coherent) scheme is giving the best results which answers the research question whether better data and model can lead to better results.• Present study indicates the technique to determine principal paths of propagation even for

complex terrain



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• In indicates techniques to incorporate accurate spatial parameters such as path-difference, ground type, angle of reflection, barrier shape etc which were not been possible previously for real outdoor sound modeling.

• It can be used for 3D sound mapping rather than conventional 2D mapping

• It can generate higher resolution sound map/sound contour



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Thank you !!!



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D. A. Bies and C.H. Hansen. Engineering Noise Control., Theory and practices Unwin Hyman (2003)

"The Propagation of Noise from Petroleum and Petrochemical Complexes to Neighbouring Communities". CONCAWE Report 4/18, (1981).

ISO 9613-2, 1996(E), ‘Acoustics-Attenuation of sound during propagation outdoor-Part 2: General method of calculation’, p.1-18.

Maekawa, Z 1968, ‘Noise reduction by screens’, Applied Acoustics, 1, p. 157–173.

RTA group (n.d.),ENM-Environmental Noise Model-Program Specification, viewed 7 December, 2007<http://www.rtagroup.com.au/enm/environmental_noise_model.html>.

Renzo Tonin 2004, Modeling and Predicting Environmental Noise, viewed 12 November 2006, <http://www.rtagroup.com.au/pdfs/22.pdf>.

Soundscape, further reading, viewed 7 september 2007, http://en.wikipedia.org/wiki/Soundscape#Further_reading

Important References

Noise Mapping, Assesment of data sources and available modeling techniques- are they good enough for comprehensive coverage by computer noise mapping? (2002), http://www.cerc.co.uk/services/Noise%20Mapping%20CERC%20IofA%20Feb2002.pdf, viewed 17 January 2007



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Coherent Addition of two 40dB Tonal Sounds at Diff. Phase Difference

Simulations to assist theoretical understanding and research findings

Phase Difference in deg.

SPL in dB



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In-Coherent Addition of two 40dB Tonal Sounds


SPLcombined_40_40=43.01 dB1

Effect of Background Noise

In-Coherent Addition of two Tonal Sounds 80dB, 40dB

SPLcombined_80_40=80.0004 dB



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SourceReceiver

Path_diff=1 mBarrier Attenuation for a

tone of 250 Hz=13.16 dB

tone of 4000 Hz=25 dB


How different frequency sounds are affected by same geometry



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High Frequency wave (shorter wavelength) 2

Low Frequency wave (higher wavelength)Spatial distribution

of Interference maxima

Wavelengths of 4 tonal sounds used

250Hz1.36 m500Hz0.68 m1000Hz0.34 m4000Hz0.085 m

Interference and Frequency



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10 m 10 m

5 m

Path_difference=2.36 m 5 m

50 m 50 m

Path_difference=0.49m

Why at shorter distance prediction is more dependent on accurate data



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Determination of Principal Path for over top of buildings –after points of intersection and vertical lines of intersection are determined ( It tries to determine path from PS to R via secondary source(s) (SS))

1. Straight line is drawn between PS and R , if PS-R line is not been intersected by any line of intersection Direct transmission else

2. All the intersecting point/line below PS-R are eliminated (if any)

3. Straight line is made between PS and tallest intersecting point, if this line is not been intersected by any line of intersection then tallest intersecting point becomes a secondary source (SS). And iteration continues from SS as above till sound reaches the receiver.

4. If this line is being intersected then, tallest amongst them becomes the SS .And iteration continues from SS as above till sound reaches the receiver.



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Determination of Principal Paths around sides of the buildings –after points of intersection and lines of intersection at side walls are determined ( It tries to determine path from PS to R via secondary source(s) (SS))

1. Line is drawn between PS to R , all lines of intersection not intersected by this line will be deleted for the current iteration. Rest of the intersecting points along with lines of intersection will be tested for finding SS

2. Among the available lines of intersections nearest one from PS is chosen first. Two intersecting points attached to it becomes the first pair of SS

3. From each of the above SS iteration continues seperately4. Straight line is drawn between SS to S and checked for finding

intersecting ‘lines of intersection’. When there is no such line or only one line belonging to same building then there is no further SS in the principal path. When there are two or more such line but all belonging to same building from which the iteration is being tested then the next SS belongs to same building. When there are two or more such line but belonging to same or different building then, it tries to select intersecting point(s) attached to nearest ‘line of intersection’ of either building maintaining criteria of shortest route to reach R.

5. Step 4 repeats iteratively till sound reaches to R

Download - Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 1 Susham Biswas & Bharat Lohani Geoinformatics Laboratory Indian Institute of Technology

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