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Analog NR (1980-90s)

Digital Noise Reduction: Understanding Lab and Real World Outcomes Ruth Bentler University of Iowa. Analog NR (1980-90s). Early spectral approaches Switch ASP ( means low frequency compression) Adaptive filtering Frequency dependant input compression Adaptive compression TM

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Analog NR (1980-90s)

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  1. Digital Noise Reduction: Understanding Lab and Real World OutcomesRuth BentlerUniversity of Iowa

  2. Analog NR (1980-90s) • Early spectral approaches • Switch • ASP (means low frequency compression) • Adaptive filtering • Frequency dependant input compression • Adaptive compressionTM • Zeta Noise BlockerTM

  3. 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.

  4. 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

  5. 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….

  6. 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

  7. With DNR shut off, can observe the “onset” of the Wiener filter (~ 3 sec)

  8. 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)

  9. 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

  10. Back to modulation-based DNR • Modulation count • Important for speech? • Typical of noise? • Modulation depth • Plomp studies • 0-100%

  11. Time waveform of a random noise

  12. Time waveform of a sample speech signal

  13. Modulation spectra Speech Noise

  14. Example of algorithm “rule #1”

  15. Example of algorithm “rule #2”

  16. DNR: What happens in the time domain?

  17. Siemens (Triano)

  18. Starkey (Axent)

  19. Widex (Diva)

  20. Sonic Natura 2 SE BTE DIR 50dB Flat Loss NOISE REDUCTION: HIGH Omnidirectional EXPANSION: OFF 85 dB Speech+ Random+Speech (0:57,1:52,2:51) Average RMS power: -43.79dB

  21. Sonic Natura 2 SE BTE DIR 50dB Flat Loss NOISE REDUCTION: HIGH Omnidirectional EXPANSION: OFF 85dB Speech+ Random+Speech (0:57,1:52,2:51) Average RMS power: -48.04dB

  22. Sonic Natura 2 SE BTE DIR 50dB Flat Loss NOISE REDUCTION: HIGH Omnidirectional EXPANSION: OFF 85 dB Speech+ Random+Speech (0:57,1:52,2:51) Average RMS Speech= -43.79dB Average RMS Noise= -48.04dB Reduction: 4.25dB

  23. 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

  24. STARKEY Axent AV 50dB Flat Loss NOISE MGMT:MAX Omnidirectional EXPANSION OFF FEEDBACK OFF 85dB Speech+ Ramdom+ Speech (0:58,1:53,2:51) Average RMS power: -44.03dB

  25. 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 Speech= -30.47dB Average RMS Noise= -44.03dB Reduction: 13.56dB

  26. Average RMS power: -31.96dB

  27. Average RMS power: -29.01dB

  28. Average RMS Speech= -31.96dB Average RMS Noise= -29.01dB Reduction(actual increase)=-2.95dB

  29. Data?

  30. 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

  31. 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

  32. 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

  33. 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)

  34. 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

  35. 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

  36. 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.

  37. 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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