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Nov 30, 2007. Audica ™. VChoice Innovation presents…. A better way to communicate ®. Team Formation. Consists of four dedicated engineers Caroline Chen Biomedical Engineer Micky Pun Computer Engineer Richard Tang Computer Engineer Jeramy Wu Computer Engineer. Contents. Background

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Nov 30, 2007


VChoice Innovation presents…

A better way to communicate®

team formation
Team Formation
  • Consists of four dedicated engineers
    • Caroline Chen
      • Biomedical Engineer
    • Micky Pun
      • Computer Engineer
    • Richard Tang
      • Computer Engineer
    • Jeramy Wu
      • Computer Engineer
  • Background
  • Design Process
  • System Integration
  • Results
  • Applications & Features
  • Project Aspects
  • Conclusion


Purpose & Vision

  • Amyotrophic lateral sclerosis (ALS) is an incurable neuromuscular disease.
  • One’s motor neurons degenerate leading to losing control over voluntary muscles.
  • 2new cases of ALS are diagnosed every year per

100 000 individuals, 2500 of which are Canadians.

  • It can happen to anyone at any time with an average of 3-5 year life expectancy after onset of the disease.
  • 20% of the diagnosed will survive more than 5 years, and longer with the advancement of medical treatments.
  • People with ALS would eventually become breathing-impaired and rely on a respiratory machine.
  • Noise produced by the respiratory machine, along with the deterioration of speech muscles, together would make their voice slurry and unrecognizable.
  • Communication is critical between a person living with ALS and his or her family members, therapist, caregivers, and anyone around.
bipap machine
BiPAP™ Machine
  • BiPAP™
    • Bi-level Positive Airway Pressure
    • A non-invasive apparatus that helps patients experiencing respiratory failure to breathe
  • Our team is proposing a speech enhancer which:
    • Works in conjunction with a BiPAP™
    • Suppresses ambient noise
    • Improves speech intelligibility
    • Promises comfort
    • Guarantees low-cost
design process

Design Process

Design and Considerations

design process11
Design Process
  • Requirements elicitations
    • Examine the BiPAP™ machine
    • Contact persons living with ALS
    • Visit PROP
  • Functional specifications
    • Three categories
      • Core
      • Less core
      • Development phase
design process12
Design Process
  • Design
    • Phase 1: Noise reduction
    • Phase 2: Intelligibility improvement
  • Simulation
    • Evaluate performance
  • Iterative implementation
    • Continuous refinements
  • Verifications and validations
    • Compare with simulation results
system integration

System Integration

Design Consideration

system integration14
System Integration
  • We have looked into multiple solutions for voice reception
    • Throat microphone
    • Microphone in mask
    • Microphone in tube
system integration15
System Integration

Throat Microphone

Microphone in mask

system integration16
System Integration
  • Inverted Y-connector
    • Small branch for microphone integration and speech reception
    • Top part is a rubber gasket
    • Connect between the mask and the BiPAP™ hose
    • Prevent wind noise from directing at microphone
system integration18
System Integration
  • For final product, there are three components:
    • Y-Connector with microphone integrated within it
    • Processing module with one button for turning the system on and off
    • Speaker with volume adjustment
speech intelligibility

Speech Intelligibility

Linear Predictive Coding

speech intelligibility21
Speech Intelligibility
  • Linear Predictive Coding
    • Adjust vowel sound
    • Re-create speech using adjusted vowel sound
  • Matlab Simulation
    • No significant improvement
    • No further investigation due to time constraint
noise reduction

Noise Reduction

Algorithm Choice

research conducted
Research Conducted
  • Research conducted by Dynastat
    • 32 participants
    • 4 classes of filters
    • 4 different environments
audica filter
Audica™ Filter
  • Statistical and wiener filters have superior performances in general
  • We adapted modified version of statistical filter at the end
    • Assumptions made about noise closely models the BiPAP noise™
  • We performed subjective listening tests,
    • Noise reduction is significant
    • Intelligibility for daily conversation is high
  • To quantize the result in numbers
    • Correlation between the input and output
      • 89.1%
    • Noise was reduced by 19.47dB (88x) !
  • The algorithm has successfully removed the noise.








applications features

Applications & Features

For Various Environments

  • We are submerged in noisy environments
    • Air conditioning
    • PC and office equipment
    • Vacuum cleaners
    • Radio channel
    • Motor vehicle noise
    • Machinery
    • Appliances, power tools
    • … etc
  • Persons using BiPAP™
    • Home and hospital
  • Industrial Workers
  • Pilots
  • Scuba Divers
  • Broadcasting Companies
c 3 comfort
C3 - Comfort
    • No physical contact between the microphone and the user to avoid extra skin break-down
  • Light-weight
c 3 convenience
C3 - Convenience
  • Simple plug-and-play design
  • Superior performance in various


    • Train station, busy intersections, and highways
c 3 cost efficiency
C3 – Cost-efficiency
  • Mass Production Cost:
    • Development kit is much more costly due to prototyping purposes
    • After consulting with a professional engineer
      • Production cost of the base unit would be ~$50
      • Microphones ~$1
      • Speakers ~$10
other features
Other Features
  • Real-time adaptive noise filter
  • Stylish casing
  • Voice Activation Detection (VAD)
project aspects

Project Aspects

Finance and Timelines

  • Proposed Project Costs vs. Actual Project Costs

Difference between Proposed/Actual costs = ($512)

  • Proposed Timeline
  • Actual Timeline


Final Thoughts & Acknowledgement

  • What could be done better?
  • Skills learned
    • Technical skills
    • Soft skills
  • Future Plans
    • Optimization of the Statistical Model Filter
      • Slower DSP can be utilized – lower cost
    • System Portability
      • System powered by rechargeable battery
    • Further Research of LPC
      • Integration of speech intelligibility into the system
  • 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. John Bird
    • Professor, School of Engineering Science, SFU
  • Dr. Patrick Leung
    • Professor, School of Engineering Science, SFU
  • Dr. Jim McEwen
    • President, ALS Society of British Columbia
  • Mr. Simon Cox
    • Executive Director, Provincial Respiratory Outreach Program
  • Mr. Les
    • Person living with ALS
  • Ms. Reva Vaze
    • Graduate Student, UBC
  • Mr. Thusjanthan (Nathan) Kubendranathan
    • System Consultant, School of Computing Science
  • Mr. Johnny Pak
    • Undergraduate Student, SFU



technical presentation

Technical Presentation

Engineering behind Audica™

system overview

System Overview

Software & Hardware

system overview49
System Overview
  • Digital System Design
    • Rapid prototyping
    • Capable of intensive numerical calculations
    • Widely supported development environment
general architecture
General Architecture
  • Interrupt driven design
  • Sampling rate of 8kHz
    • Suitable for conversation
noise reduction51

Noise Reduction

Filter Consideration & Simulation

noise reduction52
Noise Reduction
  • Filter Considerations
    • Band-Limited Filters
      • Low-pass
      • High-pass
      • Band-pass
      • Band-reject
noise reduction53
Noise Reduction
  • Band-Limited Filter
    • Advantage
      • Simple
      • Realization possible with analog circuit
    • Disadvantage
      • Noise is often not band-limited
      • BiPAP™ noise is pink
noise reduction54
Noise Reduction

BiPAP™ Noise

noise reduction55
Noise Reduction
  • Real-time Adaptive Filter
    • Spectral Subtraction
    • Wiener
    • Statistical
    • Sub-Space
noise reduction56
Noise Reduction
  • Statistical-Model
  • Optimization Criterion:
    • Minimizing mean square difference of log
  • Assume Non-linear relationship between inverse filter and noisy speech signal
algorithm details
Algorithm Details
  • Two Phases,
    • Calibration
      • Takes the first 8 frames to observe the noise pattern, or record “noise variance” so to speak
    • Continuous Process
      • Inverse filter the speech signal based on the noise spectrum recorded in the calibration state
estimating priori snr
Estimating Priori SNR
  • Decision Directed Approach
    • First term is a priori SNR, based on previous frame statistical values
    • Second term is a SNR estimate based on current frame
    • a is a weighted factor
      • Good results shown for a = 0.98
optimization criteria
Optimization Criteria
  • Use logX as minimizing criteria
  • After rigorous mathematical derivation,
  • Where is the Priori SNR, and the exponential term being exponential integral which can be solved numerically


    • Speech Enhancement by Philipos C. Loizou
    • Numerical Recipes in C by William H. Press
intelligibility improvement

Intelligibility Improvement

Linear Predictive Coding

improving intelligibility
Improving Intelligibility
  • Formant frequencies
    • Fundamental frequencies that make up vowels
  • 12 out of 40 phonemes in the English language are vowels
improving intelligibility63
Improving Intelligibility
  • Linear Predictive Coding (LPC)
    • Predictor Coefficients
      • Represents the formant frequencies
    • Prediction Residue
      • Apply to the predictor coefficients to re-synthesize the speech signal
improving intelligibility64
Improving Intelligibility
  • Roots of predictor coefficients are computed, and altered using formant frequencies table.
improving intelligibility65
Improving Intelligibility
  • New coefficients obtained using the altered roots
  • Original prediction residual is applied to the new predictor coefficients
  • The result: speech signal with formant frequencies within range of average persons
improving intelligibility66
Improving Intelligibility
  • Unfortunately we observed marginal difference when we ran the described procedure through MATLAB
  • With the given development time frame we decided not to further pursue this approach
development challenges

Development Challenges

Problems Encountered

  • Software
    • Code Composer Studio
    • Fourier Transform
    • Real-Time Integration
    • Memory Overlap
    • Performance Issue
  • LPC Implementation
    • Narrow field of research
    • Insufficient resources
  • External
    • Availability of BiPAP™ and masks
    • Time constraint
    • Limited access to people living with ALS due to their health constraint


Final Thoughts & Acknowledgement