Transcript Auditory Perception

Auditory Perception
Meena Ramani
04/09/2004
Department of Electrical & Computer Engineering
Note
For this lecture many of the slides will be
accompanied by scanned pictures shown on
the OHP from Zwicker and Fastl’s
“Psycho-acoustics facts and models” 2nd edition
Department of Electrical & Computer Engineering
Main Outline
• Anatomy of the Ear and Hearing DONE
• Auditory perception
• Hearing aids and Cochlear implants.
Extra: Direction of Arrival Estimation
Department of Electrical & Computer Engineering
Auditory perception
•
•
•
•
•
•
Shepard Tones
Masking <Detailed look>
Ohms Acoustic Law
Critical Bands
Webers law
Just Noticeable Frequency
Department of Electrical & Computer Engineering
Roger Penrose
Optical
Illusion
Ascending and Descending
Audio
Illusion
Department of Electrical & Computer Engineering
M.C. Escher
Shepard Tones
• Circularity in Judgments of Relative Pitch, Roger N. Shepard,
JASA 1964.
– Sensitivity to descending pitch
– Sensitivity to volume changes between these
pitches.
• A set of eight tones all an octave apart
• The tones simultaneously descend in pitch till half of
their original pitch.
• Jump back up to their original pitch and repeat the cycle.
• Perceive this change?
• Unique volume curve
• Effect: Seamless transition in the cycle.
• It’s all in your head!
• Omit two of the eight tones in the mid frequency range.
Department of Electrical & Computer Engineering
You know I can't hear you when the water is
running!
MASKING
Department of Electrical & Computer Engineering
Masking
• Low-frequency, broad banded sounds (like water running) will mask
higher frequency sounds which are softer at the listener's ear (a
conversational tone from across the room).
– Example 2: Truck in street
• Masking occurs because two frequencies lie within a critical band and
the higher amplitude one masks the lower amplitude signal.
• Masking can be because of broad band, narrowband noise, pure and
complex tones.
• Masking threshold
– Amount of dB for test tone to be just audible in presence of noise
See OHP Figure
Department of Electrical & Computer Engineering
Masking by Broad band noise
•
•
•
•
•
White noise- frequency independent PSD
Masked thresholds are a function of frequency.
Low and very high frequency almost same as TOQ.
Above 500Hz, thresholds increase with increase in frequency
Increasing white noise by 10dB increases masked threshold up by
10dB for frequencies >500Hz.
• =>Linear behavior of masking
• NOTE: TOQ’s frequency dependence almost completely
disappears Ear’s frequency selectivity and critical bands.
See OHP Figure
Department of Electrical & Computer Engineering
Masking by Narrow band noise
• Narrow band <=Critical BW
• Noise (constant Amplitude, Different Frequency)
– 0.25,1,4KHz
– BW: 100, 160, 700Hz
– 60dB
• Frequency dependence of threshold masked by 250Hz seems to be
broader
• Maximum value of masked threshold is lower for higher frequencies.
• Steep increase but flatter decrease
See OHP Figure
Department of Electrical & Computer Engineering
Masking by Narrow band noise (cont)
• Noise (Varying Amplitude, Fixed Frequency)
– 1KHz noise
– 20-100dB
• Slope of rise seems independent of Amplitude
• But slope of fall is dependent on amplitude
• Non-Linear frequency dependence
• Strange effect at high masker amplitudes:
– At high amplitudes ear begins to listen to anything audible!!
– Begin to hear difference noise (noise and testing tone)
See OHP Figure
Department of Electrical & Computer Engineering
Masking by Pure and Complex tones
• Pure tones:
– Below threshold of Quiet of test tone can hear only masking tone
– Above it <700hZ can hear both
– From 900-10kHz can hear only masking tone though above threshold of
hearing for test tone.
– Between 1-2kHz difference tones are also audible
– Low level masker  wider at low frequencies
– High level maskers wider at high frequencies
• Complex tones:
– Log scale distance between the partials has a larger difference at LF,
less difference at HF
– Dips correspondingly become smaller as frequency increases
– 2 octaves above highest spectral content curve approaches TOQ
See OHP Figure
Department of Electrical & Computer Engineering
Temporal Aspects of Masking
•
•
•
•
•
Previously assume long lasting test and masking sounds
Speech has a strong temporal structure
Vowels --loudest parts
Consonants faint
Often plosive consonants are masked by preceding loud vowel
Department of Electrical & Computer Engineering
Temporal Aspects of Masking (cont)
• Simultaneous Masking
• Pre-Stimulus/Backward/Premasking
– 1st test tone 2nd Masker
• Poststimulus/Forward/Postmasking
– 1st Masker 2nd test tone
Department of Electrical & Computer Engineering
Types of Masking
Simultaneous masking
– Duration less than 200ms test tone threshold increases with decrease in
duration.
– Duration >200ms constant test tone threshold
– Assume hearing system integrates over a period of 200ms
Postmasking (100ms)
– Decay in effect of masker 100ms
– More dominant
Premasking (20ms)
– Takes place before masker is on!!
– Each sensation is not instantaneous , requires build-up time
• Quick build up for loud maskers
• Slower build up for softer maskers
– Less dominant effect
See OHP Figure
Department of Electrical & Computer Engineering
Ohm’s Acoustic Law
The sound quality of a complex tone depends ONLY on the
amplitudes and NOT relative phases of its harmonics.
Department of Electrical & Computer Engineering
Critical Bands
• Proposed by Fletcher
• Noise which masks a test tone is the part of its spectrum which lies
near the tone
• Masking is achieved when the power of the tone and the power
of the noise spectrum lying near the tone and masking it are
the same.
• Bands defined this way have a BW which produces same acoustic
power in the tone and in the noise in the band when the tone is
masked. CRITICAL BANDS
See OHP Figure
Department of Electrical & Computer Engineering
Critical Band (cont.)
• How to measure?
– Masking of a band pass noise using 2 tones
• CB corresponds with1.5mm spacing on BM.
• 24 such band pass filters
• BW of the filters increases with increasing center frequency
• Logarithmic relationship  Weber’s law example.
• Bark scale
See OHP Figure
Department of Electrical & Computer Engineering
Webers law
• Weber's Law states that the ratio of the increment threshold to
the background intensity is a constant.
• So when you are in a noisy environment you must shout to be heard
while a whisper works in a quiet room.
• when you measure increment thresholds on various intensity
backgrounds, the thresholds increase in proportion to the
background.
Department of Electrical & Computer Engineering
Just noticeable change in
Frequency
•
(Pg:183)
•
•
•
•
•
Similar to variation in the critical band structure
This is because it depends on number of BPFs
More BPF better resolution
Till about 500Hz JND is about 3.6Hz.
After 500Hz it varies as 0.007f
See OHP Figure
Department of Electrical & Computer Engineering
HEARING AIDS
Department of Electrical & Computer Engineering
Outline
•
•
•
•
•
Facts on hearing loss
Cell phones and hearing loss
Types of Hearing aid
Inside a hearing aid
Audiogram
Department of Electrical & Computer Engineering
Facts on Hearing Loss in Adults
• One in every ten (28 million) Americans has hearing loss.
• The vast majority of Americans (95% or 26 million) with hearing loss
can have their hearing loss treated with hearing aids.
• Only 5% of hearing loss in adults can be improved through medical or
surgical treatment
• Millions of Americans with hearing loss could benefit from
hearing aids but avoid them because of the stigma.
Department of Electrical & Computer Engineering
Cell phones and Hearing aids
• Cell Phones emit a type of electromagnetic energy that interferes
with the operation of hearing aids.
• The Federal Communications Commission in mid-July 2003 ordered
the cell phone industry to help out the hard-of-hearing.
“Within two years, cell-phone manufacturers must offer at least
two phones with reduced interference for each type of cellular
technology used, or ensure that one-fourth of phones the
carriers sell produce less interference.”
• The FCC’s final milestone is February 2008.
Department of Electrical & Computer Engineering
Types of Hearing aids
Behind The ear
In the Canal
Department of Electrical & Computer Engineering
In the Ear
Completely in the
canal
Anatomy of a Hearing Aid
•
•
•
•
Microphone
Tone hook
Volume control
On/off switch
• Battery compartment
Department of Electrical & Computer Engineering
Inside a Hearing aid
1: The microphone
The microphone picks up sound waves from the air and transforms
them into electrical signals.
2: The microphone suspension
The microphone suspension holds the microphone in place.
3: The loudspeaker
The loudspeaker sends the amplified sounds into your ear. The
loudspeaker is also called the receiver and sometimes the telephone.
4: The battery drawer
The battery drawer holds the battery in place.
5: The amplifier
The amplifier makes the signals that come from the microphone louder.
6: The telecoil
The telecoil makes it possible for you to hear one specific person if you
are in a place that supports the use of a telecoil. Many classrooms,
churches and cinemas have telecoil. The telecoil makes it possible for
you to hear i.e. your teacher without hearing the noise around you. It is
also possible to use the telecoil at home - with the TV or the radio.
Department of Electrical & Computer Engineering
Audiograms
Department of Electrical & Computer Engineering
Direction of Arrival (DOA) estimation algorithm
<Useful for class project ideas>
Department of Electrical & Computer Engineering
Talk outline
• Necessity for DOA
• DOA algorithm Requirements
• Types of DOA algorithms
–
–
–
–
–
–
•
•
•
•
•
Delay and sum
Minimum variance
MUSIC
Coherent MUSIC
Root MUSIC
ESPRIT
Comparison Measures
Computational Intensity comparison
Accuracy Comparison
Accuracy vs Computational intensity
Conclusion
Department of Electrical & Computer Engineering
Where does the DOA come into the picture?
Lets meet
at 11?!?
DOA Estimation
qs & qn
Beamformer
Has 2 microphones
Department of Electrical & Computer Engineering
7 is good
11
forsounds
me
good!!
too!!
Direction of Arrival Estimation Algorithms
The DOA algorithm must satisfy the following conditions :
– Low computational intensity (MIPS/MFLOPS)
– High accuracy (RMSE)
– High speed
– Easy implementation
– Good performance at low SNRs
– Works on a 2 microphone array system with 4cm
separation between them.
Department of Electrical & Computer Engineering
DOA Algorithms
Spatial Correlation
methods
Delay and Sum
Subspace decomposition
methods
Minimum Variance
MUSIC
Multiple Signal Estimation
ESPRIT
Estimation of Signal parameters
using rotational invariance
Coherent MUSIC
Root MUSIC
Department of Electrical & Computer Engineering
DOA Method
Equation for Implementation
Delay and Sum
P(q )  a (q ) Sa(q )
Minimum
Variance
P(q ) 
*
1
a* (q )inv( s)a(q )
MUSIC
*
P(q ) 
Coherent
MUSIC
a (q )a(q )
a (q ) EN EN a(q )
*
P(q ) 
*
a 'a
a ' EN EN ' a
Root MUSIC
qK  sin1 c.angle( z) /(0 d )
ESPRIT
q K  sin
1
c arg(
K
) /(0 d )
Department of Electrical & Computer Engineering
Comparison Measures
• To evaluate the computational intensity
– MFLOPS comparison plot
• To evaluate the accuracy
– Root Mean Square Error comparison plot
• To evaluate the effect at low SNRs
– SNR vs Estimated angle plot
• To evaluate overall performance
– Accuracy vs computational intensity
Department of Electrical & Computer Engineering
Evaluation of computational Intensity:
MFLOPS comparison chart
Min Variance
0.93 Mflops
Coherent MUSIC
0.3958 Mflops
DS
0.3573 Mflops
MUSIC
0.0813 Mflops
ESPRIT
0.0086 Mflops
Root MUSIC
0.0068 Mflops
Department of Electrical & Computer Engineering
Comparison of accuracy at different SNR values
Comparison of Estimated DOAs: SNR=10dB,Speech= 90' ,Noise=0'
180
160
ESPRIT
RMUSIC
CMUSIC
MUSIC
MV
DS
Estimated DOA (degrees)
140
120
100
80
60
40
20
0
0
5
10
15
Frame Number
20
25
Estimated DOAS for only those regions which are speech
180
160
Estimated DOA (Degrees)
140
120
100
80
60
40
20
0
0
5
10
15
Frames Number
Department of Electrical & Computer Engineering
Comparison of Accuracy-MFLOPS
Department of Electrical & Computer Engineering
Conclusion
• Tradeoff between Accuracy and Computational intensity
leads to the conclusion that ESPRIT is the Direction of
arrival estimation algorithm best suited for our purpose
• MFLOPS value: 0.0086
• RMSE value:~3 (at 10dB)
Department of Electrical & Computer Engineering
Comparison of Estimated DOAs: SNR=10dB,Speech= 90' ,Noise=0'
180
160
ESPRIT
RMUSIC
CMUSIC
MUSIC
MV
DS
Estimated DOA (degrees)
140
120
100
80
60
40
20
0
0
5
10
15
Frame Number
20
Department of Electrical & Computer Engineering
25
Estimated DOAS for only those regions which are speech
180
160
Estimated DOA (Degrees)
140
120
100
80
60
40
20
0
0
5
10
Frames Number
Department of Electrical & Computer Engineering
15
Department of Electrical & Computer Engineering