Post on 29-May-2020
Nikolas Klakow, AuD
Customer Trainer, Phonak LLC
How do they know that?
Exploring Processing in Digital Hearing Aids
2
Agenda
Introduction
Sound Classification
Noise Reduction
Directional Microphones
3
How does the brain know what this is?
4
How we look at sounds
Time-domain graph
Spectrum
Spectogram
5
Fast Fourier Transform
The FFT is an algorithm for computation of discreet Fourier transform (DFT).
Allows a system to work in the frequency domain
Basic calculation that will be used for:
Signal amplification
Feedback cancellation
Noise reduction
Adaptive directionality
Spectral enhancement/reduction
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Frequency Analysis
Compound Signal
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/eh/
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Frequency Analysis - 2
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The vowels /eh/ /a/
/e/ /oh/
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Trivia
When was the Fast Fourier Transform discovered?
1866
1805
1918
1956
German Mathematician Carl Friedrich Gauss credited with discovery
11
Agenda
Introduction
Sound Classification
Noise Reduction
Directional Microphones
12
Sound Classification
Speech
Music
Noise
13
What is that?
14
Sound Classification
Periodicity
Spectral Envelope/Prediction
Statistical Evaluation
15
Periodicity
Periodic sounds:
Music generally periodic
Vowels
Some consonant i.e. /n/
Aperiodic sounds
Sibilants
Noise
Schaub, A. Digital Hearing Aids, 2008
16
Autocorrelation
How does short signal segment match previous one?
Coefficient quantifies correspondence between segments with values between +1 to -1
What is the period of example (a)?
Schaub, A. Digital Hearing Aids, 2008
8.5ms
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Autocorrelation Coefficient
Value close to +1
indicates matching segments
Value around 0
indicates completely different segments
Value close to -1
indicates identical waveforms, but opposite phase
/n/
Schaub, A. Digital Hearing Aids, 2008
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What is that?
19
Autocorrelation of samples
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AC for Speech
Y-axis in Figure (b) shows delay between signal segments.
Period increases gradually from 9 to 10ms
Corresponds to fundamental freq. from ~110 to 100Hz changing slowly: speech trait
Schaub, A. Digital Hearing Aids, 2008
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AC for Pulsed Noise
Noise from dishes in sink.
Waveform alone has modulation like speech
Lack of correlation suggest signal is not speech
Schaub, A. Digital Hearing Aids, 2008
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How about this?
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Autocorrelation of samples - 2
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AC for Music
Easy Listening music for flute and guitar.
Continuous periodicity allows system to differentiate between noise, even if signal has little modulation.
Schaub, A. Digital Hearing Aids, 2008
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AC for Steady Noise
Minimal modulation
High AC (0.9), but period change irregularly between 10 and 16ms
Corresponds to fast changes of fundamental freq. from 60 to 100Hz
Typical of low freq noise, not speech.
Schaub, A. Digital Hearing Aids, 2008
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Sound Classification
Periodicity
Spectral Envelope/Prediction
Statistical Evaluation
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Spectral Envelope
Greater precision with higher order envelopes
Predictability becomes a parameter
Schaub, A. Digital Hearing Aids, 2008
Spectrum
16th order envelope
2nd order envelope
1st order envelope
/s/
/ae/
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Normalized Prediction Error
Nearer to 1 – flatter spectrum
Nearer to 0 – more pronounced resonances
Schaub, A. Digital Hearing Aids, 2008
Music
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Normalized Prediction Error - 2
Schaub, A. Digital Hearing Aids, 2008
Music Steady Noise
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Putting clues together
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Sound Classification
Periodicity
Spectral Envelope/Prediction
Statistical Evaluation
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Statistical Evaluation
Using statistical analysis systems can determine likelihood of sounds being music, noise, or speech based on attributes:
Periodicity, fundamental frequency, spectrum gradient, dominant resonance
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Sound Classification
Speech
Music
Noise
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Agenda
Introduction
Sound Classification
Noise Reduction
Directional Microphones
35
Noise Reduction
Speech and noise – what to do?
36
Block diagram of NR bands
Band-pass filter
High-pass filter
Low-pass filter
Determine attenuation
Determine attenuation
Determine attenuation
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Divide and process
0-500 Hz
500-2000 Hz
2-8 kHz
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Maxima and minima
Measure max/min modulation in discreet bands
Maxima
Minima
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Modulation
Subtract minima from maxima
Schaub, A. Digital Hearing Aids, 2008
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ANSI S3.5-1997: The SII
Speech Intelligibility Index
Between 0 and 1
Indicates proportion of speech cues available
Conditions to reach “1”
1. Level of speech signal ≤ 10dB above normal speech at distance of 1 meter
2. Speech signal must exceed noise by 15+dB
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The 15dB rule
Modulation > 15dB
no attenuation
Modulation < 15dB
attenuated by difference between 15dB and own modulation
Modulation (dB)
0 0
5
5 10
10
15
15
Att
enuation (
dB)
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Modulation
Subtract minima from maxima
Schaub, A. Digital Hearing Aids, 2008
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Attenuation values
Varying attenuation in bands
Schaub, A. Digital Hearing Aids, 2008
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Bands after attenuation
Schaub, A. Digital Hearing Aids, 2008
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And the difference…
Before NR
After NR
Schaub, A. Digital Hearing Aids, 2008
46
Thoughts on NR
Should it be SPL dependent?
How does it interact with WDRC?
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More Trivia
The Fourier Transform was named in honor of the French mathematician, Joseph Fourier (1768-1830)
He is credited with the discovery of the Greenhouse effect.
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Agenda
Introduction
Sound Classification
Noise Reduction
Directional Microphones
49
The Directional Pattern
Front
Back
1.Cardioid
2.Hypercardioid
3.Supercardioid
4.Dipole
Null = azimuth at which there is the least amplification
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Dual Omnidirectional Microphones
Benefits:
Adaptive polar patterns
Multiple polar patterns
d
T
xF(t)
xR(t-T)
Difference signal
d(t) Front
Rear
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Directionality Basics
Speed of sound : 340 m/s (c)
Microphone separation (d) : 8.5 mm
Time delay between microphones = 25 μs
Electronic Delay (T): 25 μs
Cardioid pattern – cancellation at 180°
d
T
xF(t)
xR(t-T)
Difference signal
d(t) Front
Rear
-
+
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Math Alert!
8.5 mm = 0.0085 m (d)
Speed of Sound 340 m/s (c)
0.0085 m : 340 m/s = 0.000025 s
0.000025 s = 25μs
d/c provides the time it takes the sound to
get from one microphone to the other.
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Sound from Behind
Hypothetical calculation:
Sound enters rear mic with value 100
Sound enters front mic with value of 75 (25 delay)
Electronic delay T= 25
Therefore : (xF(t) = 75) – (xR(t-T) = 75) = 0
d
T
xF(t)
xR(t-T)
Difference signal
d(t) Front
Rear
Sound
+
-
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Sound from Front
Hypothetical calculation:
Sound enters front mic with value 100
Sound enters rear mic with value of 75 (25 delay)
T= 25
Therefore : (xF(t) = 100) – (xR(t-T) = 50) = 50
d
T
xF(t)
xR(t-T)
Difference signal
d(t) Front
Rear
Sound
+
-
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Demonstration Signal
4000Hz
2000Hz
1000Hz
500Hz Compound Waveform
Spectrum
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Sound from the Front (75dB)
a) Microphone signals
b) Difference signal
Schaub, A. Digital Hearing Aids, 2008
Output signal spectra
57
Sound from the Side (75dB)
Output signal spectra
a) Microphone signals
b) Difference signal
Schaub, A. Digital Hearing Aids, 2008
58
Adapting Directional Pattern Nulls
Without changing T, polar pattern could not redirect null for maximum reduction of signal
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Adaptive Beamforming
Green-difference signal front-cardioid pattern dF(t)
Yellow-difference signal rear-cardioid pattern dR(t)
Blue-variable directional pattern output signal y(t)
Red-calculates microphone gain (b) defining null direction
Schaub, A. Digital Hearing Aids, 2008
d
xF(t)
xR(t)
dF(t) Front
Rear dR(t)
d/c
d/c Self-
Adjustment
y(t) +
-
+
+
-
-
b
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Variable Directional Patterns
Equation:
y(t) = dF(t) – (b · dR(t))
b = 1 b = 0.5
Schaub, A. Digital Hearing Aids, 2008
61
Self-Adjusting Directionality
Speech sample used for following examples
Schaub, A. Digital Hearing Aids, 2008
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Self-Adjusting Directionality - 2
Difference signals of Speech in
Noise-Cardioid pattern.
(a) Front dF(t)
(b) Rear dR(t)
Schaub, A. Digital Hearing Aids, 2008
63
Directional patterns and output signals for varying values of factor b
Self-Adjusting Directionality - 3
b = 0.13
b = 0.53
Schaub, A. Digital Hearing Aids, 2008
64
This b value reduces enough noise to allow stop consonants /t/ and /d/ to be audible.
Self-Adjusting Directionality - 4
b = 0.33
Schaub, A. Digital Hearing Aids, 2008
Original
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Questions?
Kates, J.M. Digital Hearing Aids, Plural Publishing, 2008
Schaub, A. Digital Hearing Aids, Thieme Publishing, 2008
Dillon, H. Hearing Aids, Thieme Publishing, 2001