Susham Biswas & Bharat Lohani Geoinformatics Laboratory Indian Institute of Technology Kanpur Kanpur, 208016 INDIA 2 - PowerPoint PPT Presentation

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Susham Biswas & Bharat Lohani Geoinformatics Laboratory Indian Institute of Technology Kanpur Kanpur, 208016 INDIA 2

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

  2. Representation of Outdoor Sounds and/or its applications Noise map Around Airport Animation Videography-movie Impulsive sound propagation i.e., gun shot, bomb blast etc Urban Noise Map (e.g., UK Noise Map)

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

  4. Sound modeling Sound Modeling Sound Input Spatial Parameter Sound Model Sound Output

  5. Outdoor Sound Propagation Transmitted Wave Sound Absorbed Wave Reflector Obstruction Diffracted Wave Reflected Wave Ground Ground Reflected Wave

  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

  7. Sound Modeling (Semi_emperical) How sound can reach receiver R from source PS Direct Transmission Direct transmission of sound from source to receiver

  8. How sound can reach receiver R from source PS Ground Reflected Sound transmission after ground reflection

  9. Diffracted over and around sides of building How sound can reach receiver R from source PS

  10. How sound can reach receiver R from source PS Wall reflected Transmission through reflection from wall

  11. How sound can reach receiver R from source PS Tree absorbed

  12. Spatial information required in sound modeling !! 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 Distance between Source* (PS) and Receiver (R) Path_difference for Diffraction Path for Ground_reflection with ground type Possibility of Wall_reflection 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)

  13. Poor Sound Model Sound model (Important components) Distance attenuation Building attenuation Spatial Parameter Where N: Fresnel Number Atmospheric attenuation

  14. Poor Sound Model Sound model (Important components) Ground attenuation 3 Zone approach (ISO-9613-2) Complex approach Ground reflecting point dependent Spatial Parameter Tree attenuation

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

  16. Objectives of research Can Lidar data be used for sound modeling- how? Is there any Advantage of using high resolution spatial data? Whether Better Data and/ or Model can lead to better prediction?

  17. Prediction of Sound at Outdoor with Poor Data and Poor Model Observed Sound at outdoor Prediction of Sound at Outdoor with Good Data and Poor Model Comparison Can LiDAR data & Aerial Photograph be used for Sound Modeling? Prediction of Sound at Outdoor with Good Data and Good Model Field Measurement Sound Prediction Schemes • 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? • Methodology

  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

  19. Spatial Survey Design of Algorithms Design of Model Spatial Parameters Measured Field Data Sound Parameters at Source Predicted Sound for outdoor location Sound Model Non-Spatial Parameters Measured Field Data • Sound Prediction Scheme Methodology Contd.

  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

  21. How to do LiDAR data points Building points Ground points Tree points Classification Triangulation / cluster formation/ Other Building corner points and building edges Ground TIN TIN from tree points cluster Coordinates of source and receiver Determination of principal propagation paths Cutting plane technique Modeling parameters Ground type attribute Classified aerial photo Sound Model Receiver sound level Source sound level

  22. Aim Determination of all principal paths for propagation

  23. Determination of Principal Path due to diffraction (over top)

  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

  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

  26. Determination of Principal Path due to diffraction (over top) Finding effective intersecting points

  27. Determined of Principal Path due to diffraction (over top) Determined principal path over top (i.e., PS-c-d-R)

  28. Determination of Principal Path due to diffraction (around sides) Cutting plane technique

  29. Determination of Principal Path due to diffraction (around sides) Intersecting points at cutting plane

  30. Determination of Principal Path due to diffraction (around sides) Intersecting points & line of intersection Intersecting points and line of intersection between building side walls and cutting plane

  31. Determination of Principal Path due to diffraction (around sides) Intersection points with projected lines of intersection Intersecting points and lines of intersection projected on XY plane

  32. e Determination of Principal Path due to diffraction (around sides) h a d g f b c Principal paths around sides (i.e., PS-a-e-h-R and PS-b-c-R)

  33. Different Routes in complex Scenario

  34. Eight Principal path or routes between any pair of Source and Receiver Source 5 & 6 Source Receiver 1 Source Receiver 2 Receiver Source Source Receiver 3 7 & 8 Source Receiver 4 Receiver

  35. Determination of Principal Path for ground reflection Comparison of TIN planes with PS-R vertical plane Classified ground point Triangulation

  36. Determination of Principal Path stretch causing tree absorption Finding TIN planes intersected by PS-R line Classified tree points K-Mean clustering Triangulation

  37. Finding the point of reflection (if any) Reflecting Point Source Receiver The plane of “Source – Reflecting Point – Receiver” The plane of “Wall”

  38. Spatial Data Prediction Scheme with Poor Model Dist S-R Path-Diff Avg. Gr.Type Predic ted SPL ) = ( AAtm + - + AB AG + SPLS AD

  39. Prediction Scheme with Good Model1 Spatial Data • 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 Aveg. AR AD . . . . . . Coherent ABG . . . Predicted SPL - . SPLS = + + + + + + + + + + 1DI 1AD 1ABG 1AAtm 1AR 1Aveg. 8DI 8AD 8ABG 8AAtm 8AR 8Aveg.

  40. Prediction Scheme with Good Model2 Spatial Data 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 Aveg. AR AD . . . Incoherent . . . ABG . . . Predicted SPL - . SPLS = + + + + + + + + + + 1DI 1AD 1ABG 1AAtm 1AR 1Aveg. 8DI 8AD 8ABG 8AAtm 8AR 8Aveg.

  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

  42. Validation experiment 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

  43. Propagated Sound Measured Sound at Receiver Spatial Survey Experiment-Measurement of Sound and Spatial data Reflector for TS Measured Sound at Source Total Station

  44. Experimental Detail- Sound Generation Computer (Generation of Tonal Sound) Speaker (output device) Amplifier

  45. Experimental Detail- Sound Measurement (at receiver) Sound Measured simultaneously at a fixed position at source as well

  46. Measured SPL in dB dB Frequency=250Hz

  47. Predicted SPL in dB (for Good data and Good Model1) dB Frequency=250Hz

  48. Deviation between measured & predicted Good data – Good Model1 -250 Hz Deviation in dB

  49. Deviation between measured & predicted Good data – Good Model1 500 Hz Deviation in dB

  50. Deviation between measured & predicted Good data – Good Model1 1000 Hz Deviation in dB