Analog NR (1980-90s)
description
Transcript of Analog NR (1980-90s)
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Digital Noise Reduction: Understanding Lab and Real
World OutcomesRuth Bentler
University of Iowa
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Analog NR (1980-90s) Early spectral approaches
Switch ASP (means low frequency compression) Adaptive filtering Frequency dependant input compression Adaptive compressionTM
Zeta Noise BlockerTM
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Today’s versions Most are modulation-based with some algorithm for
where and how much gain reduction should occur; At least one other (Oticon) first introduced a strategy
called “synchronous morphology” to determine when noise reduction will occur;
Several are now implementing Wiener filters as well Many also use some mic noise reduction, expansion,
wind noise reduction, and even directional mics as part of the strategy they promote.
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Today’s talk Focus on DNR Defined here as modulation-based noise
reduction Difficult to “un-involve” the other noise
reduction approaches currently implemented Circuit noise Wind Noise etc
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Let’s focus on the impact of Wiener filtering… Norbert Wiener, Missouri-born theoretical
and applied mathematician; developed filter in the early 1940s, published in 1949
VERY interesting fellow….
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Let’s focus on the impact of Wiener filtering… The input to the Wiener filter is assumed to be a
signal, s(t), corrupted by additive noise, n(t). The output, x(t), is calculated by means of a filter, g(t), using the following convolution: x(t) = g(t) * (s(t) + n(t))
…where s(t) is the original signal (to be estimated) n(t) is the noise x(t) is the estimated signal (which we hope will equal s(t)) g(t) is the Wiener filter
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With DNR shut off, can observe the “onset” of the Wiener filter (~ 3 sec)
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Let’s focus on the impact of Sound SmoothingTM… Intended to reduce negative effect of short transient
sounds, such as a door slamming, or cutlery clattering;
Steepness of the envelope slope used to determine if speech or noise (both have crests or peaks)
Very fast time constants; across multiple channels Evidence to support use (Keidser et al, 2007)
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How do ‘classification systems’ fit in here? Many high end products have what are
referred to as “classifiers” to categorize the environment for feature activation;
The classification process is likely to impact the onset of many features, esp DNR automatic/adaptive mic schemes Other speech enhancement strategies
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Back to modulation-based DNR Modulation count
Important for speech? Typical of noise?
Modulation depth Plomp studies 0-100%
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Time waveform of a random noise
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Time waveform of a sample speech signal
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Modulation spectra
Speech
Noise
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Example of algorithm “rule #1”
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Example of algorithm “rule #2”
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Frequency (Hz)
125 250 500 1000 2000 4000 8000
Diff
eren
ce (d
B,1
/3oc
tave
)
-25
-20
-15
-10
-5
0
5
GN ReSound (CANTA 770-D)
a
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Frequency (Hz)
125 250 500 1000 2000 4000 8000
Diff
eren
ce (d
B,1
/3oc
tave
)
-25
-20
-15
-10
-5
0
5
ICRA SpeechRandom NoiseBabble
Starkey (AXENT II AV MM)
b
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Siemens (TRIANO 3)
Frequency (Hz)
250 500 1000 2000 4000 8000
Diff
eren
ce (d
B, 1
/3 O
ctav
e)
-12
-10
-8
-6
-4
-2
0
2
SIREN TRAFFICDINING
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Unitron (CONVERSA.NT MODA 10A)
Frequency (Hz)
125 250 500 1000 2000 4000 8000
Diff
eren
ce (d
B S
PL
RM
S)
-10
-8
-6
-4
-2
0
2
4
SNR00 SNR05 SNR10SNR15
a
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Sonic Innovations (INNOVA)
Frequency (Hz)
125 250 500 1000 2000 4000 8000
Diff
eren
ce (d
B S
PL
RM
S)
-10
-8
-6
-4
-2
0
2
4
SNR00 SNR05 SNR10SNR15
b
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Oticon (ADAPTO)
Frequency (Hz)
125 250 500 1000 2000 4000 8000
Diff
eren
ce (d
B,1
/3oc
tave
)
-25
-20
-15
-10
-5
0
5
a
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Starkey J13 Axent AV75 dB
--SPEECH,RANDOM, MUSIC--
Frequency(Hz)
125 250 500 1000 2000 4000 8000
DIF
FER
EN
CE
(dB
,1/3
octa
ve)
-25
-20
-15
-10
-5
0
5
GuitarPianoSaxophone with background musicRandom NoisePlain Speech
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DNR: What happens in the time domain?
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Siemens (Triano)
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Starkey (Axent)
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Widex (Diva)
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Sonic Natura 2 SE BTE DIR 50dB Flat Loss
NOISE REDUCTION: HIGHOmnidirectional
EXPANSION: OFF 85 dB Speech+ Random+Speech
(0:57,1:52,2:51)
Average RMS power: -43.79dB
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Average RMS power: -48.04dB
Sonic Natura 2 SE BTE DIR 50dB Flat Loss
NOISE REDUCTION: HIGHOmnidirectional
EXPANSION: OFF85dB Speech+ Random+Speech
(0:57,1:52,2:51)
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Reduction: 4.25dB
Average RMS Speech= -43.79dBAverage RMS Noise= -48.04dB
Sonic Natura 2 SE BTE DIR 50dB Flat Loss
NOISE REDUCTION: HIGHOmnidirectional
EXPANSION: OFF 85 dB Speech+ Random+Speech
(0:57,1:52,2:51)
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STARKEY Axent AV 50dB Flat Loss
NOISE MGMT:MAX Omnidirectional EXPANSION OFF FEEDBACK OFF 85dB Speech+ Random+ Speech (0:58,1:53,2:51)
Average RMS power: -30.47dB
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Average RMS power: -44.03dB
STARKEY Axent AV 50dB Flat Loss
NOISE MGMT:MAX Omnidirectional EXPANSION OFF FEEDBACK OFF 85dB Speech+ Ramdom+ Speech (0:58,1:53,2:51)
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STARKEY Axent AV 50dB Flat Loss
NOISE MGMT:MAX Omnidirectional EXPANSION OFF FEEDBACK OFF 85dB Speech+ Random+ Speech (0:58,1:53,2:51)
Reduction: 13.56dB
Average RMS Speech= -30.47dBAverage RMS Noise= -44.03dB
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Average RMS power: -31.96dB
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Average RMS power: -29.01dB
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Reduction(actual increase)=-2.95dB
Average RMS Speech= -31.96dBAverage RMS Noise= -29.01dB
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Data?
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Data? Walden et al (2000)
Single-blinded, within subject, crossover design 40 HI subjects
Omni versus directional versus directional + NR Self reported:
Speech understanding: NR+D = D = O Sound quality: NR+D = D = O Sound comfort: NR+D > O
Bottom line: Sound comfort evidence
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Data? Boymans & Dreschler (2000)
Single-blinded, within subject, crossover design 16 subjects Lab data: NR = No NR Field trials of 4 weeks (APHAB)
All subscales: NR = No NR Three aversiveness questions: NR> No NR
Bottom line: Some reduced aversiveness
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Data? Alcantara et al (2003)
Eight experienced HI HA users wore new aid for 3 months
No improvement for SRTs; no decrement for sound quality while listening to four different kinds of background noise, all in lab
Bottom line: No reduction in sound quality
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Data? Ricketts & Hornsby (2005)
14 adults, single-blinded, lab data only 2 speech-in-noise conditions
71 dBA speech, +6 SNR 75 dBA speech, +1 SNR
No effect on speech perception Bottom line: Significant preference for DNR
sound quality in lab (forced choice)
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Bentler et al (2007) Lab and field study
25 subjects 3-4 weeks field trials with 4 conditions of NR
Fast onset (~4 sec) Medium onset (~8 sec) Slow onset (~16 sec) Noise reduction turned off
Another 3-4 weeks (with “paired comparison”) of three time constants accessed by memory button
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Bentler et al (2007) AV (Aversiveness) subscale showed unaided
and NR-off to be significantly different (i.e., unaided and NR-on had similar aversiveness scores)
Diary entries indicate easier listening Bottom line: Less aversiveness and easier
listening relative to DNR-off, both in lab and in field
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Examples from diaries: #05
Off: Traffic, TV too loud On: Could hear in conversations with 20 people
#07 Off: Environmental sounds quite loud and did not notice
with other settings On: Seem to have less background noise
#09 Off: Difficult to hear in noise On: Could hear husband in restaurant and understand
almost everything #12
Off: Background and outside noises seemed louder & overpowering
On: Aid seemed to filter out noises almost to the point that
conversation was too low.
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What about kids? Current study underway to assess impact of
DNR on novel word learning, speech perception, and sound quality in young children (ages 4-10)
Evidence (in adults) that novel word learning not impaired (Marcoux et al. 2006)
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These and other data summarized: Each company has their approach
Often determined by own philosophy Confined by other features (“overhead”)
The outcomes of those different approaches are very different in both the frequency and temporal domains
Does not appear to alter sound quality, speech perception or word learning
Probably makes listening easier Need to verify DNR performance!
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Is it functioning as intended?
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Is it functioning as intended?
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So what’s a clinician to do? Know your product (whose responsibility??) Verify performance
Probe mic measures of gain/output, watch speech, magnitude and frequency distribution of the gain reduction. Same is possible (maybe even necessary) in the test box.
Also can use music passages, babble noise, etc, to observe effect
LISTEN, listen, listen…
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Questions?