Downloaded 465 times
Uploaded byMark Billinghurst
COMP 4010 Lecture5 VR Audio and Tracking
Lecture 5 covers virtual reality audio and tracking technologies, highlighting the importance of sound in enhancing realism in VR experiences. It details various audio display types, sound properties, and tracking methods, including mechanical, magnetic, inertial, acoustic, and optical tracking technologies. The lecture emphasizes the need for effective tracking to ensure immersion by mapping the user's physical movements to a virtual environment.
In this document
Powered by AI
Introduces the lecture on VR audio and tracking, recap of last week focusing on various display types and the introduction of audio displays.
Discusses audio importance in VR realism, sound capturing techniques, types of audio recordings, and properties of audio displays.
Explains audio channels, masking effects, audio displays formats like binaural, and the distinction between spatialization and localization.
Focuses on audio localization methods using HRTFs, constructions of HRTFs, and individual variations in user response to sound.
Analyzes acoustic effects in environments, sound reverberation, processing challenges, and the need for efficient sound systems.
Highlights audio display hardware advancements, including custom HRTFs and modern consumer sound cards for better audio experiences.
Explores tools for designing spatial audio in VR, achievements in projects like spatial audio design and demos in VR environments.Introducing tracking as a key element in enhancing immersion in VR, defining tracking frames and degrees of freedom.
Elaborates on key metrics for tracking performance including static and dynamic accuracy, latency, jitter, and technologies used.
Details traditional tracking methods including mechanical, magnetic, inertial, and their pros/cons, and industry examples.
Explains inertial tracker capabilities and acoustic tracking methods, including challenges faced and examples of implementations.
Discusses optical tracking technologies, hybrid systems that combine methods, and specific examples like Vive tracking.
Wraps up the presentation with essential links for additional resources and contact information.
More Related Content
Similar to COMP 4010 Lecture5 VR Audio and Tracking
![UiPath Automation Suite Installation (Hands-On) [2/3]](https://cdn.slidesharecdn.com/ss_thumbnails/automationsuitecommunitysession2-251015095633-a6d862f1-thumbnail.jpg?width=600ounds&width=560&fit=bounds)
COMP 4010 Lecture5 VR Audio and Tracking
- 1. LECTURE 5: VRAUDIO AND TRACKING COMP 4010 – Virtual Reality Semester 5 – 2016 Bruce Thomas, Mark Billinghurst University of South Australia August 23rd 2016
- 2. Recap – LastWeek • Visual Displays • Head Mounted Display • Vive, Mobile VE • Projection/Large Screen Display • CAVE, Allosphere • Haptic Displays • Active Haptics • Actively Resist Motion • Passive Haptics • Physical Props • Tactile Displays • Vibrating actuators
- 3.
- 4. Audio Displays Definition: Computerinterfaces that provide synthetic sound feedback to users interacting with the virtual world. The sound can be monoaural (both ears hear the same sound), or binaural (each ear hears a different sound) Burdea, Coiffet (2003)
- 5. Virtual Reality AudioOverview • https://www.youtube.com/watch?v=yUlnMbxTuY0
- 6. Motivation • Most of thefocus in Virtual Reality is on the visuals • GPUs continue to drive the field • Users want more • More realism, More complexity, More speed • However sound can significantly enhance realism • Example: Mood music in horror games • Sound can provide valuable user interface feedback • Example: Alert in training simulation
- 7. Creating/Capturing Sounds • Soundscan be captured from nature (sampled) or synthesized computationally • High-quality recorded sounds are • Cheap to play • Easy to create realism • Expensive to store and load • Difficult to manipulate for expressiveness • Synthetic sounds are • Cheap to store and load • Easy to manipulate • Expensive to compute before playing • Difficult to create realism
- 8. Types of AudioRecordings • Monaural: Recording with one microphone – no positioning • Stereo Sound: Recording with two microphones placed several feet apart. Perceived sound position as recorded by microphones. • Binaural: Recording microphones embedded in a dummy head. Audio filtered by head shape. • 3D Sound: Using tiny microphones in the ears of a real person. Generate HRTF based on ear shape and audio response.
- 9. Synthetic Sounds • Complexsounds can be built from simple waveforms (e.g., sawtooth, sine) and combined using operators • Waveform parameters (frequency, amplitude) could be taken from motion data, such as object velocity • Can combine wave forms in various ways • This is what classic synthesizers do • Works well for many non-speech sounds
- 10. Combining Wave Forms • Adding up waves creates new waves
- 11. Digital Audio WorkstationSoftware • Software for recording, editing, producing audio files • Mixing console, synthesizer, waveform editor, etc • Wide variety available • https://en.wikipedia.org/wiki/Digital_audio_workstation
- 12. Typical Audio DisplayProperties Presentation Properties • Number of channels • Sound stage • Localization • Masking • Amplification Logistical Properties ! Noise pollution ! User mobility ! Interface with tracking ! Environmental requirements ! Integration ! Portability ! Throughput ! Cumber ! Safety ! Cost
- 13. Channels and Masking • Numberof channels • Stereo vs. mono vs. quadrophonic • 2.1, 5.1, 7.1 • Two kinds of masking • Louder sounds mask softer ones • We have too many things vying for our audio attention these days! • Physical objects mask sound signals • Happens with speakers, but not with headphones
- 14. Audio Displays: Head-worn EarBuds On Ear Open Back Closed Bone Conduction
- 15. Audio Displays: RoomMounted • Stereo, 5.1, 7.1, 11.1, etc • Sound cube 11.1 Speaker Array
- 16. Spatialization vs. Localization • Spatializationis the processing of sound signals to make them emanate from a point in space • This is a technical topic • Localization is the ability of people to identify the source position of a sound • This is a human topic, i.e., some people are better at it than others.
- 17. Stereo Sound • Seemsto come from inside users head • Follows head motion as user moves head
- 18. 3D Spatial Sound • Seems to be external to the head • Fixed in space when user moves head • Has reflected sound properties
- 19. Spatialized Audio Effects • Naïveapproach • Simple left/right shift for lateral position • Amplitude adjustment for distance • Easy to produce using consumer hardware/software • Does not give us "true" realism in sound • No up/down or front/back cues • We can use multiple speakers for this • Surround the user with speakers • Send different sound signals to each one
- 20. Example: The BoomRoom • Use surround speakers to create spatial audio effects • Gesture based interaction • https://www.youtube.com/watch?time_continue=54&v=6RQMOyQ3lyg
- 21. Audio Localization • Main cuesused by humans to localize sound: 1. Interaural time differences: Time difference for sound wave to travel between ears 2. Interaural level differences: For high frequency sounds (> 1.5 kHz), volume difference between ears used to determine source direction 3. Spectral filtering done by outer ears: Ear shape changes frequency heard
- 22. Interaural Time Difference • Takes fixed time to travel between ears • Can use time difference to determine sound location
- 23. Spectral Filtering Ear shapefilters sound depending on direction it is coming from. This change in frequency determines sound source elevation.
- 24. Natural Hearing vs.Headphones • Due to ear shape natural hearing provides different audio response depending on sound location
- 25. Head-Related Transfer Functions(HRTFs) • A set of functions that model how sound from a source at a known location reaches the eardrum
- 26. More About HRTFs • Functionstake into account, • Individual ear shape • Slope of shoulders • Head shape • So, each person has his/her own HRTF! • Need to have a parameterizable HRTFs • Some sound cards/APIs allow specifying an HRTF
- 27.
- 28. Constructing HRTFs • Small microphonesplaced into ear canals • Subject sits in an anechoic chamber • Can use a mannequin's head instead • Sounds played from a large number of known locations around the chamber • HRTFs are constructed for this data • Sound signal is filtered through inverse functions to place the sound at the desired source
- 29. Constructing HRTFs • Puttingmicrophones in Manikin or human ears • Playing sound from fixed positions • Record response
- 30. How HRTFs areUsed • HRTF is the Fourier transform of the in-ear microphone audio response (head related impulse response (HRIR)) • From HRTF we can calculate pairs of finite impulse response (FIR) filters for specific sound positions • One filter per ear • To place virtual sound at a position, apply set of FIR filters for that position to the incoming sound
- 31. HRTF Processing • Inputsound is convolved with FIR to generate L/R outputs
- 32. Environmental Effects • Sound isalso changed by objects in the environment • Can reverberate off of reflective objects • Can be absorbed by objects • Can be occluded by objects • Doppler shift • Moving sound sources • Need to simulate environmental audio properties • Takes significant processing power
- 33. Sound Reverberation • Needto consider first and second order reflections • Need to model material properties, objects in room, etc
- 34. The Tough Part • All of this takes a lot of processing • Need to keep track of • Multiple (possibly moving) sound sources • Path of sounds through a dynamic environment • Position and orientation of listener(s) • Most sound cards only support a limited number of spatialized sound channels • Increasingly complex geometry increases load on audio system as well as visuals • That's why we fake it ;-) • GPUs might change this too!
- 35. Sound Display Hardware • Designed to reduce CPU load • Early Hardware • Custom HRTF • Crystal River Engineering Convolvotron (1988) • Real time 3D audio localizer, 4 sound sources • Lake Technology (2002) • Huron 20, custom DSP hardware, $40,000 • Modern Consumer Hardware • Uses generic HRTF • SoundBlaster Audigy/EAX • Aureal A3D/Vortex card
- 36. Convolvotron Block Diagram ForN sound sources
- 37. GPU Based AudioAcceleration • Using GPU for audio physics calculations • AMD TrueAudio Next • https://www.youtube.com/watch?v=Z6nwYLHG8PU
- 38. Audio Software SDKs • Modern CPUs are fast enough spatial audio can be generated without dedicated hardware • Several 3D audio SDKs exist • OpenAL • www.openal.org • Open source, cross platform • Renders multichannel three-dimensional positional audio • Google VR SDK • Android, iOS, Unity • https://developers.google.com/vr/concepts/spatial-audio • Oculus • https://developer3.oculus.com/documentation/audiosdk/latest/ • Microsoft DirectX, Unity, etc
- 39. Google VR SpatialAudio Demo • https://www.youtube.com/watch?v=I9zf4hCjRg0&feature=youtu.be
- 40. OSSIC 3D AudioHeadphones • 3D audio headphones • Calibrates to user – calculates HRTF • Integrated head tracking • Multi-driver array providing sound to correct part of ear • Raised $2.7 million on Kickstarter • https://www.ossic.com/3d-audio/
- 41. Ossic vs. TraditionalHeadphone • Provides frequency reproduction of real sound
- 42. OSSIC vs. GenericHeadphone • Sound source localization (T = target)
- 43.
- 44. Designing Spatial Audio • There are several tools available for designing 3D audio • E.g. Facebook Spatial Workstation • Audio tools for cinematic VR and360 video • https://facebook360.fb.com/spatial-workstation/ • Spatial Audio Designer • Mixing of surround sound and 3D audio • http://www.newaudiotechnology.com/en/products/spatial-audio-designer/
- 45. Demo: Spatial AudioIn VR • AltspaceVR spatial audio for speaker discrimination • https://www.youtube.com/watch?v=dV3Qog44z6E
- 46.
- 47. Immersion and Tracking • Motivation:For immersion, when the user changes position in reality the VR view also needs to change • Requires tracking of the user’s pose (position/orientation) in the real world and mapping to the Virtual World
- 48. Definitions • Tracking: measuring the positionand orientation of an object relative to a known frame of reference • VR Tracker: technology used in VR to measure the real time change in a 3D object position and orientation (1968) Ivan Sutherland Mechanical Tracker
- 49. • Frames ofReference • Real World Coordinate System (Wcs) • Head Coordinate System (Hcs) • Eye Coordinate System (Ecs) • Need to create a mapping between Frames • E.g. Transformation from Wcs to Hcs to Ecs • Movement in real world maps to movement in Ecs frame Frames of Reference
- 50. Example Frames ofReference Assuming Head Tracker mounted on HMD Assuming tracking relative to fixed table object
- 51. Tracking Degrees ofFreedom • Typically 6 Degrees of Freedom (DOF) • Rotation or Translation about an Axis 1. Moving up and down 2. Moving left and right 3. Moving forward and backward 4. Tilting forward and backward (pitching); 5. Turning left and right (yawing); 6. Tilting side to side (rolling).
- 52. Key Tracking PerformanceCriteria • Static Accuracy • Dynamic Accuracy • Latency • Update Rate • Tracking Jitter • Signal to Noise Ratio • Tracking Drift
- 53. Static vs. DynamicAccuracy • Static Accuracy • Ability of tracker to determine coordinates of a position in space • Depends on sensor sensitivity, errors (algorithm, operator), environment • Dynamic Accuracy • System accuracy as sensor moves • Depends on static accuracy • Resolution • Minimum change sensor can detect • Repeatability • Same input giving same output
- 54. Tracker Latency, UpdateRate • Latency: Time between change in object pose and time sensor detects the change • Large latency (> 10 ms) can cause simulator sickness • Larger latency (> 50 ms) can reduce VR immersion • Update Rate: Number of measurements per second • Typically > 30 Hz
- 55. Tracker Jitter, Signalto Noise Ratio • Jitter: Change in tracker output when tracked object is stationary • Range of change is sensor noise • Tracker with no jitter reports constant value if tracked object stationary • Makes tracker data changing randomly about average value • Signal to Noise Ratio: Signal in data relative to noise • Found from calculating mean of samples in known positions
- 56. Tracker Drift • Drift:Steady increase in tracker error over time • Accumulative (additive) error over time • Relative to Dynamic sensitivity over time • Controlled by periodically recalibration (zeroing)
- 57. Tracking Technologies • Mechanical • Physical Linkage • Electromagnetic • Magnetic sensing • Inertial • Accelerometer, MEMs • Acoustic • Ultrasonic • Optical • Computer Vision • Hybrid • Combination of Technologies Contact-less Contact-based
- 58. MechanicalTracker • Idea: mechanical armswith joint sensors • ++: high accuracy, low jitter, low latency • -- : cumbersome, limited range, fixed position Microscribe Sutherland
- 59. Example: Fake SpaceBoom • BOOM (Binocular Omni-Orientation Monitor) • Counterbalanced arm with 100 o FOV HMD mounted on it • 6 DOF, 4mm position accuracy, 300Hz sampling, < 5 ms latency
- 60. Demo: Fake SpaceTele Presence • Using Boom with HMD to control robot view • https://www.youtube.com/watch?v=QpTQTu7A6SI
- 61. MagneticTracker • Idea: Measure differencein current between a magnetic transmitter and a receiver • ++: 6DOF, robust, accurate, no line of sight needed • -- : limted range, sensible to metal, noisy, expensive Flock of Birds (Ascension)
- 62. Example: Polhemus Fastrak • Degrees-of-Freedom: 6DOF • Number of Sensors: 1-4 • Latency: 4ms • Update Rate: 120 Hz/(num sensors) • Static Accuracy Position: 0.03in RMS • Static Accuracy Orientation: 0.15° RMS • Range from Standard Source: Up to 5 feet or 1.52 meters • Extended Range Source: Up to 15 feet or 4.6 meters • Interface RS-232 or USB (both included) • Host OS compatability GUI/API Toolkit 2000/XP • http://polhemus.com/motion-tracking/all-trackers/fastrak
- 63. Polhemus Tracker Demo • https://www.youtube.com/watch?v=7DlEfd0VH_o
- 64.
- 65. Example: Razer Hydra • Developed by Sixense • Magnetic source + 2 wired controllers • Short range (< 1 m), Precision of 1mm and 1 o • 62Hz sampling rate, < 50 ms latency • $600 USD
- 66. Razor Hydra Demo • https://www.youtube.com/watch?v=jnqFdSa5p7w
- 67. InertialTracker • Idea: Measuring linearand angular orientation rates (accelerometer/gyroscope) • ++: no transmitter, cheap, small, high sample rate, wireless • -- : drift, hysteresis, noise, only 3DOF IS300 (Intersense) Wii Remote
- 68. Types of InertialTrackers • Gyroscopes • The rate of change in object orientation or angular velocity is measured. • Accelerometers • Measure acceleration. • Can be used to determine object position, if the starting point is known. • Inclinometer • Measures inclination, ”level” position. • Like carpenter’s level, but giving electrical signal.
- 69. Example: MEMS Sensor • Uses spring-supported load • Reacts to gravity and inertia • Changes its electrical parameters • < 5 ms latency, 0.01 o accuracy • up to 1000Hz sampling • Problems • Rapidly accumulating errors. • Error in position increases with the square of time. • Cheap units can get position drift of 4 cm in 2 seconds. • Expensive units have same error in 200 seconds. • Not good for measuring location • Need to periodically reset the output
- 70. Demo: MEMS SensorWorking • https://www.youtube.com/watch?v=9eSnxebfuxg
- 71. MEMS Gyro BiasDrift • Zero reading of MEMS Gyro drifts over time due to noise
- 72. Example: iPhone Sensors • Three-axis accelerometer • Gives direction acceleration - affected by gravity and movement • Three-axis gyroscope • Measures translation and rotation moment – affected by movement • Three axis magnetometer • Gives (approximate) direction of magnetic north • GPS • Gives geolocation – multiple samples over time can be used to detect direction and speed iPhone Sensor Monitor app
- 73. Acoustic - UltrasonicsTracker • Idea:Timeof Flight or Phase-Coherence Sound Waves • ++: Small, Cheap • -- : 3DOF, Line of Sight, Low resolution, Affected by Environment (pressure, temperature), Low sampling rate Ultrasonic Logitech IS600
- 74. Acoustic Tracking Methods • Two approaches: • Time difference, • Phase difference • Time-of-flight (TOF): • All current commercial systems • Time that sound pulse travels is proportional to distance from the receiver. • Problem: differentiating the pulse from noise. • Each transmitter works sequentially – increased latency. • Phase coherent approach (Sutherland 1968): • No pulse, but continuous signal (~50 kHz) • Many transmitters on different frequencies • Sent and received signal phase differences give continuously the change in distance, no latency, • Only relative distance, cumulative & multi-path errors possible.
- 75. Acoustic Tracking Principles • Measurements are based on triangulation • Minimum distances at transmitter and receiver required. • Can be a problem if trying to make the receiver very small. • Each speaker is activated in cycle and 3 distances from it to the 3 microphones are calculated, 9 distances total. • Tracking performance can degrade when operating in a noisy environment. • Update rate about 50 datasets/s • Time multiplexing is possible • With 4 receivers, update rate drops to 12 datasets/s
- 76. Example: Logitech HeadTracker • Transmitter is a set of three ultrasonic speakers - 30cm from each other • Rigid and fixed triangular frame • 50 Hz update, 30 ms latency • Receiver is a set of three microphones Placed at the top of the HMD • May be part of 3D mice, stereo glasses, or other interface devices • Range typically about 1.5 m • Direct line of sight required • Accuracy 0.1 o orientation, 2% distance
- 77. OpticalTracker • Idea: Image Processingand ComputerVision • Specialized • Infrared, Retro-Reflective, Stereoscopic • ++: Long range, cheap, immune to metal • -- : Line of Sight,Visual Targets, Low Sampling rate ART Hi-Ball
- 78.
- 79. OpticalTrackingTechnologies • Scalable active trackers • InterSenseIS-900, 3rd Tech HiBall • Passive optical computer vision • Line of sight, may require landmarks • Can be brittle. • Computer vision is computationally-intensive 3rd Tech, Inc.
- 80. Example:HiBallTracking System (3rdTech) • Inside-Out Tracker • $50K USD • Scalable over large area • Fast update (2000Hz) • Latency Less than 1 ms. • Accurate • Position 0.4mm RMS • Orientation 0.02° RMS
- 82. Example: Microsoft Kinect • Outside-in tracking • Components: • RGB camera • Range camera • IR light source • Multi-array microphone • Specifications • Range 1-6m • Update rate 30Hz • Latency 100ms • Tracking resolution < 5mm • Range Camera extracts depth information and combines it with a video signal
- 83. Hybrid Tracking • Idea:Multiple technologies overcome limitations of each one • A system that utilizes two or more position/orientation measurement technologies (e.g. inertial + vision) • ++: Robust, reduce latency, increase accuracy • -- : More complex, expensive Intersense IS-900 Ascension Laser Bird
- 84. Example: Intersense IS-900 • Inertial Ultrasonic Hybrid tracking • Use ultrasonic strips for position sensing • Intertial sensing for orientation • Sensor fusion to combine together • Specifications • Latency 4ms • Update 180 Hz • Resolution 0.75mm, 0.05 o • Accuracy 3mm, 0.25 o • Up to 140m2 tracking volume • http://www.intersense.com/pages/20/14
- 85. Demo: IS-1200 andIS-900 • https://www.youtube.com/watch?v=NkYLlTyuYkA
- 86. Example: Vive LighthouseTracking • Outside-in hybrid tracking system • 2 base stations • Each with 2 laser scanners, LED array • Headworn/handheld sensors • 37 photo-sensors in HMD, 17 in hand • Additional IMU sensors (500 Hz) • Performance • Tracking server fuses sensor samples • Sampling rate 250 Hz, 4 ms latency • 2mm RMS tracking accuracy • Large area - 5 x 5m range • See http://doc-ok.org/?p=1478
- 87. Lighthouse Components • sd Basestation - IR LED array - 2 x scanned lasers Head Mounted Display - 37 photo sensors - 9 axis IMU
- 88.
- 89. How Lighthouse TrackingWorks • Position tracking using IMU • 500 Hz sampling • But drifts over time • Drift correction using optical tracking • IR synchronization pulse (60 Hz) • Laser sweep between pulses • Photo-sensors recognize sync pulse, measure time to laser • Know when sensor hit and which sensor hit • Calculate position of sensor relative to base station • Use 2 base stations to calculate pose • Use IMU sensor data between pulses (500Hz) • See http://xinreality.com/wiki/Lighthouse
- 90. Lighthouse Tracking Base stationscanning https://www.youtube.com/watch?v=avBt_P0wg_Y https://www.youtube.com/watch?v=oqPaaMR4kY4 Room tracking
- 91.