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Anti-Snoring Pillow (ASP). For a peaceful night of sleep. December 13, 2007. LifeX Team. Raymond Lee Software Researching parts Camillia Lee Documentation Software Testing Simon Wong Theory Software Debugger Stanley Yang Software Budget. Outline. Background Objectives

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Anti snoring pillow asp

Anti-Snoring Pillow (ASP)

For a peaceful night of sleep

December 13, 2007


Lifex team
LifeX Team

  • Raymond Lee

    • Software

    • Researching parts

  • Camillia Lee

    • Documentation

    • Software

    • Testing

  • Simon Wong

    • Theory

    • Software

    • Debugger

  • Stanley Yang

    • Software

    • Budget


Outline
Outline

  • Background

  • Objectives

  • System Overview

  • High Level System Design

  • Business Case

  • Results

  • What was learned

  • Future Improvements

  • Conclusion



Background
Background

“Forty-five percent of normal adults snore at least occasionally, and 25 percent are

habitual snorers.”

“Thirty percent of adults over age 30 are snorers. By middle age, that number reaches 40 percent.”


Background continued
Background… continued

  • A number of effects to both the snorer and those who hear him/her

  • daytime drowsiness,

  • irritability,

  • lack of focus,

  • decrease libido

  • psychological and social damage


Current existing solutions
Current existing solutions

  • Surgeries, sleeping aids, dental appliances

  • Downfalls

    • Expensive

    • Invasive

    • Painful

    • Complications

    • Unreliable



Objectives
Objectives

  • Produce a affordable non-invasive solution to reduce the sound of snoring

    Goal: Minimize snoring noise at low frequencies by 10-15dB


Lifex s solution
LifeX’s Solution

The “Anti-Snoring Pillow”

-A noise suppression system integrated into a pillow



Types of noise control passive
Types of Noise Control - Passive

  • Reduces noise using specialized materials

    • Sound isolation

    • Sound absorption

    • Vibration damping

  • i.e. Ear muffs


Types of noise control active
Types of Noise Control - Active

  • Acoustic cancellation that involves a control speaker for emitting a opposite polarity sound


Adaptive anc
Adaptive ANC

  • Adaptive ANC

    • Real time controller for monitoring the system’s performance

    • System parameters are always changing

    • Required for complex noise (i.e. speech, snoring, random noise, etc)


Adaptive anc1
Adaptive ANC

  • How?

    • Digital signal travels faster than speed of sound!

  • Advantages over passive acoustic control

    • More effective at low frequencies

    • Less bulky

    • Able to block noise selectively

    • A “good” system will yield better performance (up to 20+dB reduction)

    • Adaptive!!!


System overview
System Overview

  • 1x Speaker (Control)

  • 2x Microphone (Reference & Error)

  • 1x DSP board

  • 1x Pillow




Active noise cancellation systems
Active Noise Cancellation Systems

  • Types of ANC system

  • Digital Filters

  • Adaptation Algorithm


Types of anc system
Types of ANC System

  • Two Major types

    • Waveform synthesis (Periodic noise – Engine noise, fan noise)

    • Adaptive Filtering

      • Feedback (No reference signal)

      • Feedforward (Reference signal)

        • Feedforward is always preferred over feedback when reference signal is available



Feedforward system
Feedforward System

Adaptive broadband feedforward control with an acoustic input sensor


Digital filters
Digital Filters

  • Finite Impulse Response (FIR)

    • Inherently stable

  • Infinite Impulse Response (IIR)

    • Built in feedback compensation

    • Less computational low

    • Can model complex systems

      • Inherently unstable


Digital filters1
Digital Filters

  • Three major parameters: type of system, filter weights, number of filter weights

    • Optimization by trial and error


Adaptation algorithm
Adaptation Algorithm

  • Least Mean Square (LMS)

  • FXLMS

    • Secondary path compensation (Offline Training)


Adaptation algorithm1
Adaptation Algorithm

  • Filtered-U Recursive (RLMS)



Market
Market

  • Our target market would be towards couples sleeping on the same bed

  • Our anti-snoring product is unique compared to other solutions available

  • Benefits to our product

    • Non-invasive

    • Inexpensive

    • Safe

    • Comfortable

    • User friendly



Financing
Financing

  • Bank loans

    • Investment banking

  • Private investors

  • Angel investors


Competition
Competition

  • High performance passive ANC foam ear plugs

  • Chin-up Strips

    • Keeps mouth closed to reduce snoring

  • Nasal strips

    • Keep nostrils opened for better breathing

  • Surgery

  • None using Active Noise Cancellation!!!






Results
Results

  • Sine waves








Future improvements
Future Improvements

  • Try more algorithms

  • Automatic Gain Control

  • Faster convergence rate for complex audio processing

  • Controllable pre-amplifier and output-amplifier


Future improvements cont
Future Improvements – cont.

  • More suitable equipment

    • Low frequency Omni-directional microphones

    • Low frequency speakers

  • Perform testing in a controlled environment

  • Wideband ANC

    • Solution: Multi-channel System!



What was learned
What was learned

  • Time management

    • Mike was wrong! “Take what you think and multiply it by 3.”

      • …More like by 8

  • Team work

  • DSP

  • Active Noise Cancellation

  • Documentation

  • Ideas to Product


Conclusion
Conclusion

  • Target more complex sounds

  • Automatic Gain Control

  • Stability

  • Solutions…

    • Multi-channel System!

    • Omni-directional Microphones

    • Low frequency speakers

    • More optimization!!


References
References

  • [1] American Physical Therapy Association, “Physical Therapy Patient Satisfaction Questionnaire Research Grants”, 2007, http://www.apta.org//AM/Template.cfm?Section=Home

  • [2] Texas Instruments, “Design of Active Noise Control System with the TMS320 Family, June 1996, http://focus.ti.com/lit/an/spra042/spra042.pdf

  • [3] Speech Vision Robotics group , “Finite Impulse Response Filters”, http://svr-www.eng.cam.ac.uk/~ajr/SA95/node13.html

  • [4] TMS320C6713 DSK - Technical Reference. Stafford, TX: Spectrum Digital Inc., 2004.

  • [5] A DSP/BIOS AIC23 Codec Device Driver for the TMS320DM642 EVM, Texas Instrument, June 2003, http://focus.ti.com/lit/an/spra922/spra922.pdf

  • [6] “Sampling rate” – Wikipedia, September 2007, http://en.wikipedia.org/wiki/Sampling_rate

  • [7] “Understanding Active Noise Cancellation”, Colin N Hansen, 2001

  • [8] "Headphones." Frontech - Best of Its Kind. 2006. 1 Nov. 2007 <http://www.frontechonline.com/headphones.html>.

  • [9] "X-540." Logitech. 2007. 1 Nov. 2007 <http://www.logitech.com/index.cfm/speakers_audio/home_pc_speakers/devices/234&cl=ca,en>.

  • [10]“Latex Pillows, Foam Pillows for Head and Neck”, AllergyBuyersClub. 2007 <http://www.allergybuyersclubshopping.com/latex-head-neck-pillows.html>

  • [11] “A Host Port Interface Board to Enhance the TMS320C6713 DSK” Morrow, M.G.; Welch, T.B.; Wright, C.H.G. May 2006 <http://ieeexplore.ieee.org>.


Acknowledgement
Acknowledgement

  • Dr. Andrew Rawicz

    • Wighton Professor for Engineering Development, School of Engineering Science, SFU

  • Mr. Mike Sjoerdsma

    • Lecturer, School of Engineering Science, SFU

  • Mr. Brad Oldham

    • Teaching Assistant, School of Engineering Science, SFU

  • Ms. Lisette Paris-Shaadi

    • Teaching Assistant, School of Engineering Science, SFU

  • Dr. Lakshman One

    • Professor, School of Engineering Science, SFU





Secondary path estimation
Secondary Path Estimation

E = fir_out - adaptfir_out; //error signal

adaptfir_out +=(c[i]*dly_adapt[i]); //adaptive filter filter output

c[i] = c[i]+(beta*E*dly_adapt[i]); //update weights of adaptive filter


Fxlms implementation
FXLMS Implementation

A[n] = 0.9999*A[n]+(muA*En*X[n]); //update weights of adaptive FIR

Xp[0] += (w[l]*X[l]);

Y[0] +=(A[i]*X[i])*10000; //adaptive FIR filter output


Leaky implementation
Leaky Implementation

Roundoff and quantization error can accumulate and cause coefficients to grow out of the allowed range (overflow)

A[n] = 0.9999*A[n]+(muA*En*X[n]); //update weights of adaptive FIR








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