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Audio Measurements

Audio Measurements. Su, Amit, David, Muthu. Outline. Microphone Introduction to Win CE Audio data collecting with iPAQ Audio data analysis. Introduction to microphones. What is microphone? Microphone is a transducer -- an energy converter.

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Audio Measurements

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  1. Audio Measurements Su, Amit, David, Muthu

  2. Outline • Microphone • Introduction to Win CE • Audio data collecting with iPAQ • Audio data analysis

  3. Introduction to microphones • What is microphone? • Microphone is a transducer -- an energy converter. • It senses acoustic energy (sound) and translates it into equivalent electrical energy. • How it works? • Dynamic Microphones • Good • reliability, need little maintenance • fairly good signal-to-noise ratio • Bad • no "tailored" response

  4. How it works? • Condenser Microphones • Good: high-quality performance • Ability to respond to transient sounds • extended high-frequency response • weigh less smaller • Bad • sensitive to mechanical noise • Other Types of Microphones • Ribbon microphone • Phantom Power

  5. How to choose microphone • Microphone specifications • Decibel (dB) scale • Measures how sensitive the microphone is. • Frequency Response • “Bandwidth“ of microphone • Multiple frequency response • “Bandwidths“ for sound coming from different directions • On-axis response • Response to sound coming directly to the microphone • Off-axis responses • Response to sound coming from all angles

  6. Microphone specifications • Diffuse field response • Response to sound coming from reflections • Polar Response • how certain frequencies are reproduced when they enter the microphone from a circle • Equivalent noise level • noise from microphone itself (good if <15db) • Sensitivity • what voltage a microphone will produce at a certain sound pressure level • SPL handling capability (Sound pressure level) • Where a certain Total Harmonic Distortion (THD) occurs. • Where the signal from the microphone will clip, that is the waveforms will become squares.

  7. Outline • Microphone • Introduction to Win CE • Audio data collecting with iPAQ • Audio data analysis

  8. Windows CE Architecture • Windows CE Design Principles • Small Memory • Modular Approach • Processor Portability • Win32 Compatibility • Comprehensive Development Tool Support • Connectivity • Real Time Processing • Win32 Programming Model • Utilises a large subset of the Win32 API (No Win16 support) • Supports MFC, VC and VB (eMbedded)

  9. Remote Connectivity Windows CE Shell Services WIN32 APIs COREDLL, WINSOCK, OLE, COMMCTRL, COMMDLG, WININET, TAPI IrDA Kernel Library TCP/IP GWES File Manager Device Manager File drivers Drivers Devicedrivers OAL Bootloader Windows CE Architecture OEM ISV, OEM Microsoft Applications Embedded Shell OEMHardware

  10. Developer Issues • Windows CE Memory Model • Protected Address Space • Virtual Memory • Memory Allocation • Stack • Heap • Virtual Memory (VirtualAlloc) • Memory mapped files • Processes and Threads • No process priority classes • Threads with the same priority run in a round-robin fashion • Number of threads only limited by available memory 4GB 3GB 2GB 1GB 0GB Reserved for system Memory Mapped Files Process slot 32 (32MB) . . . . . . Process Slot 2 (32MB) Process Slot 1(32MB) Process Slot 0 (32MB) 64KBGuard

  11. Developer Issues • File System • No Concept of Current Directory • No Support for Overlapped I/O • Support for Installable and Remote File Systems • Power Issues • Porting Win32 Applications • Unicode • GDI differences • User interface issues – e.g. no mouse • Tool Support • eMbedded Visual C++ • eMbedded VB • Visual Studio .NET

  12. Outline • Microphone • Introduction to Win CE • Audio data collecting with iPAQ • Audio data analysis

  13. My own experience • Life cycle on data analysis • Background • Difficulties • Achievement • Demo

  14. Audio data • What is audio data • To human: something you can hear • To computer: digital signals • What is audio data features • Energy • zero-crossing • Spectrum • ……

  15. Where audio data being used? • Engineering Acoustics • Acoustic signal processing • musical sounds synthesis and composition • Physical Acoustics • Ultrasonics and infrasonics • Propagation of sound through the atmosphere, fluids, and fluid-filled materials • Psychological and Physiological Acoustics • Speech Recognition and Generation • Physiology and biophysics of the ear, the auditory nerve, and higher neural centers • Others • Acoustical Oceanography • Architectural Acoustics

  16. Data collecting procedure • Tools used in our data collecting • iPAQ build in mirophone • Microsoft embedded C++ • How? • On iPAQ • Record, compress, send • On server • Receive, unzip, concat

  17. Difficulties • Which recorder is better? • Windows build in recorder control vs. self-developed wav recorder • Why choosing self-developed wav recorder? • Guessing …

  18. Measure accuracy • Channels – one or two data stream • Mono • Stereo • Bit per sample – how good each sample is • 8 bit • 16 bit • Sample rate – how many samples are taken each second? • 8.0 kHz (telephone quality) • 11.025 kHz • 22.05 kHz (FM radio quality) • 44.1 kHz (CD quality) • File size • Channel * Bit/sample * Sample rate * sample time

  19. Procedure • Record • Prepare • Open a connection with the device using this handle • Allocate a buffer for incoming data • Reading data • Write to wave file • Compress/Uncompress • Standard zip/unzip • Send/Receive • Sockets similar to ftp

  20. Achievements • Let us do the demo now…

  21. Future Improvements • Better headset • Better Compression • More efficient algorithm? • Online zipping • Make data streaming • Weakness • Each file length is limited by iPAQ memory • Total recording depends on wireless link • Your own file format • What if wireless link is broken?

  22. Outline • Microphone • Introduction to Win CE • Audio data collecting with iPAQ • Audio data analysis

  23. Data Cleaning and Analysis • What is noise? • From textbook • Sound - the occurrence of an audible event • Noise – nonperiodic sound • To us • Sound – signal data we are interested in • Noise – signal data that is useless to us • How to remove noise? • Example 1- data are mixed. pick up certain people’s voice while he is talking with a group. • Example 2 – data are sparse. Is there any cell phone rings during a 3 hours meeting?

  24. Data Processing • Given voice samples, what can we get from it? • Volumn • Picth • Spectrum • … • What can we do with it?

  25. Audio data from Telcordia

  26. What is pattern inside?

  27. Future Work • On-line processing • Server side • Client side - Fat sensor? • Fusing network

  28. Future Work • Data Annotation?

  29. Applications • Location detector • Scenario 1: Prof. Muthu is sleeping on the train, but he does not worry about missing New Brunswick… • Smart filter • Scenario 2: Prof. Muthu preparing his lecture notes on the train. And David will call him around that time. He does not want to be interrupted except that call…

  30. A little test for fun • Given voice samples from David, Amit and Su. Can you tell them apart?

  31. A little hint

  32. Result • 1- Amit • 2 – David • 3 - Su

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