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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 1 Susham Biswas & Bharat Lohani Geoinformatics Laboratory Indian Institute of Technology Kanpur Kanpur, 208016 INDIA 20 th January, 2011 Sound Propagation Modeling at High Resolution Using LiDAR Data and Aerial Photograph for Outdoor environments

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 2 Noise map Around 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 3 Outdoor Sounds and its Relationship with Spatial and other Data : Modeling aspect

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 4 Sound Modeling Sound Model Sound Input Spatial Parameter Sound Output Sound modeling

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 5 Outdoor Sound Propagation Obstruction Diffracted Wave Reflector Reflected Wave Ground Reflected Wave Ground Sound Transmitted Wave Absorbed Wave

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 6 Sound Modeling Numerical 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 7 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 8 Ground Reflected Sound transmission after ground reflection How sound can reach receiver R from source PS

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 9 Diffracted over and around sides of building How sound can reach receiver R from source PS

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 10 Wall reflected Transmission through reflection from wall How sound can reach receiver R from source PS

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 11 Tree absorbed How sound can reach receiver R from source PS

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 12 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 Diffraction 3.Path for Ground_reflection with ground type 4.Possibility of Wall_reflection 5.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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 13 Sound model (Important components) Where N: Fresnel Number Distance attenuation Building attenuation Poor Sound Model Atmospheric attenuation Spatial Parameter

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 14 Poor Sound Model Sound model (Important components) Spatial Parameter Ground attenuation 3 Zone approach (ISO-9613-2) Complex approach Ground reflecting point dependent Tree attenuation

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 15 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 16 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 17 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 18 Methodology Field Measurement Sound Measurement Spatial data Measurement Non_Spatial data Measurement Sound Prediction Schemes Poor Data and Poor Model Good Data and Poor Model Good Data and Good Model Comparative Analysis

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 19 Sound Model Spatial Parameters Non-Spatial Parameters Sound Parameters at Source Spatial Survey Measured Field Data Design of Model Design of Algorithms Predicted Sound for outdoor location Sound Prediction Scheme Methodology Contd.

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 20 Design of Algorithm to extract Spatial Parameters Data 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 21 LiDAR data points Modeling parameters Source sound level Sound Model Receiver sound level Classification Triangulation / cluster formation/ Other Cutting plane technique Ground pointsBuilding pointsTree points Building corner points and building edges Ground TINTIN 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 22 Aim Determination of all principal paths for propagation

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 23 Determination of Principal Path due to diffraction (over top)

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 24 Determination of Principal Path due to diffraction (over top) Cutting plane technique Cutting plane orthogonal to X-Y plane and passing through source and receiver

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 25 Determination of Principal Path due to diffraction (over top) Intersecting points Intersecting points with vertical line of intersection between buildings and cutting plane Intersecting points & vertical line of intersection

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 26 Determination of Principal Path due to diffraction (over top) Finding effective intersecting points

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 27 Determined of Principal Path due to diffraction (over top) Determined principal path over top ( i.e., PS-c-d-R)

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 28 Determinati on of Principal Path due to diffraction (around sides) Cutting plane technique

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 29 Determinati on of Principal Path due to diffraction (around sides) Intersecting points at cutting plane

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 30 Determinati on of Principal Path due to diffraction (around sides) Intersecting points and line of intersection between building side walls and cutting plane Intersecting points & line of intersection

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 31 Determinati on of Principal Path due to diffraction (around sides) Intersecting points and lines of intersection projected on XY plane Intersection points with projected lines of intersection

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 32 Determinati on of Principal Path due to diffraction (around sides) Principal paths around sides Principal paths around sides (i.e., PS-a-e-h-R and PS-b-c-R) a bc d e f g h

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 33 Different Routes in complex Scenario

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 34 1 2 3 4 5&65&6 7&87&8 Eight Principal path or routes between any pair of Source and Receiver SourceReceiver SourceReceiver SourceReceiver SourceReceiver Source Receiver Source Receiver

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 35 Determination of Principal Path for ground reflection Classified ground pointTriangulation Comparison of TIN planes with PS-R vertical plane

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 36 Determination of Principal Path stretch causing tree absorption Classified tree points K-Mean clustering Triangulation Finding TIN planes intersected by PS-R line

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 37 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 38 Dist S-R Path-Diff Avg. Gr.Type Spatial Data Prediction Scheme with Poor Model - ADAD ABAB AGAG + + SPL S ( )= Predic ted SPL A Atm +

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 39 Prediction Scheme with Good Model1.. - SPL S = 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 1AD1AD 1 A BG 1 A Atm + + 1 DI + 1AR1AR + 1 A veg. + A BG.. 8AD8AD 8 A BG 8 A Atm + + 8 DI + 8AR8AR + 8 A veg. + CoherentCoherent ADAD.. ARAR.. A veg...

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 40 Prediction Scheme with Good Model2.. - SPL S = 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 1AD1AD 1 A BG 1 A Atm + + 1 DI + 1AR1AR + 1 A veg. + A BG.. 8AD8AD 8 A BG 8 A Atm + + 8 DI + 8AR8AR + 8 A veg. + IncoherentIncoherent ADAD.. ARAR.. A veg... Spatial Data

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 41 Design of Experiment Experimental Setups Sound Generation Sound Measurement Position Measurement Experimental Parameters Frequency of sound SPL/Leq Experimental Schedule Experiment Primary Experiment Auxiliary Experiment

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 42 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 43 Measured Sound at Receiver Propagated Sound Spatial Survey Measured Sound at Source Total Station Reflector for TS Experiment-Measurement of Sound and Spatial data

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 44 Computer (Generation of Tonal Sound) Amplifier Speaker (output device) Experimental Detail- Sound Generation

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 45 Experi- mental Site Parameter studied Frequency (Hz) of Study No. of Position No. of Ht/ position Total No. Observation Duration of measurem ent at each observ- ation Metero- logical Condition 130mX 30m, At Air Strip, having building and different ground types SPL250, 500, 1000, 4000 703 (0.7m,1.35 m,1.75m) 84690 secNear Neutral Experimental Detail- Sound Measurement (at receiver) Sound Measured simultaneously at a fixed position at source as well

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 46 Measured SPL in dB dB Frequency=250Hz

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 47 dB Frequency=250Hz Predicted SPL in dB (for Good data and Good Model1)

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 48 Good data – Good Model1 -250 Hz Deviation in dB Deviation between measured & predicted

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 49 Good data – Good Model1 500 Hz Deviation in dB Deviation between measured & predicted

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 50 Good data – Good Model1 1000 Hz Deviation in dB Deviation between measured & predicted

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 51 Good data – Good Model1 4000 Hz Deviation in dB Deviation between measured & predicted

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 52 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 53 MeanSTDMaxMin Freq 2507.126.5539.870.01 Good Data & Good Model 1 5008.467.1134.020.03 Good Data & Good Model 1 10008.357.3541.990.04 Good Data & Good Model 1 40009.598.7542.000.03 Good Data & Good Model 1 2507.757.4642.540.01 Good Data & Good Model 2 5009.228.3239.550.00 Good Data & Good Model 2 10009.098.4644.940.07 Good Data & Good Model 2 400010.869.3944.940.03 Good Data & Good Model 2 25011.999.4552.730.15 Good Data & Poor Model 50016.6412.4251.620.02 Good Data & Poor Model 100010.599.0947.490.11 Good Data & Poor Model 400010.169.4941.990.10 Good Data & Poor Model 25012.869.6552.240.15 Poor Data & Poor Model 50016.8512.8051.290.04 Poor Data & Poor Model 100011.149.5347.010.06 Poor Data & Poor Model 400010.429.3341.510.07 Poor Data & Poor Model Comparison of Different prediction Schemes Statistical Analysis

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 54 Frequ- ency Probability of H 0 to be true at 0.05 significance level 250 Hz1.11*10 -16 500 Hz0 1000 Hz0.0018 4000 Hz0.5354 250 Hz 500 Hz1000 Hz 4000 Hz ANOVA statistical test for four prediction schemes At least one of the four prediction schemes giving different results Statistical Analysis Abbreviations used M1=Good data & Model1 M2=Good Data & Model2 GP=Good Data & Poor Model PP= Poor Data & Poor Model Comparison of Deviation µ(D) M1 = H0:H0: µ(D) M2 =µ(D) GP =µ(D) PP

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 55 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 2503.0810.9812.3110.9114.492.66 5003.4914.3813.7414.6215.950.54 10003.287.997.974.828.041.39 40004.422.162.461.541.500.51 All7.1917.5618.0313.5817.592.38 Paired ‘t’ Test Abbreviations used M1=Good data & Model1 M2=Good Data & Model2 GP=Good Data & Poor Model PP= Poor Data & Poor Model Statistical Analysis Comparison of different pairs of prediction schemes in terms of deviation t(0.05,120)=1.645 H0:H0: µ(D) 1 = µ(D) 2 Generally the pairs of prediction schemes are not giving similar results

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 56 2508.31 5008.31 10009.67 400010.98 Error in Deviation Freq. Propagation of Error Data analysis

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 57 Hence, generally the prediction scheme involving Good Data & Good Model1(Coherent) seems to performed the best Analysis

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 58 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 59 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 60 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 61 Perception of a street noise at different spatial location Audio Realization G T Road IIT Gate

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 62 Discussion and Conclusion LiDAR/Aerial Photo data can be use to incorporate detail terrain information foroutdoor 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 63 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 64 Thank you !!!

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 65 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. 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 2007http://www.cerc.co.uk/services/Noise%20Mapping%20CERC%20IofA%20Feb2002.pdf

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 66

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 67 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 68 In-Coherent Addition of two 40dB Tonal Sounds Simulations to assist theoretical understanding and research findings SPL combined_40_40 =43.01 dB11 Effect of Background Noise In-Coherent Addition of two Tonal Sounds 80dB, 40dB SPL combined_80_40 =80.0004 dB

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 69 Source Receiver Path_diff=1 m Barrier Attenuation for a tone of 250 Hz=13.16 dB tone of 4000 Hz=25 dB Simulations to assist theoretical understanding and research findings How different frequency sounds are affected by same geometry

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 70 High Frequency wave (shorter wavelength) 22 Low Frequency wave (higher wavelength) Spatial distribution of Interference maxima Wavelengths of 4 tonal sounds used 250Hz 1.36 m 500Hz 0.68 m 1000Hz 0.34 m 4000Hz 0.085 m Interference and Frequency

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 71 10 m 5 m Path_difference=2.36 m 5 m 50 m Path_difference=0.49m Why at shorter distance prediction is more dependent on accurate data

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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 72 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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Indian Institute of Technology Kanpur Susham Biswas Susham Biswas 73 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 seperately 4.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

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