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Understanding Digital Noise Reduction: Lab and Real World Outcomes

This talk explores the impact of digital noise reduction (DNR) techniques in hearing aids, including Wiener filtering and sound smoothing. The talk also discusses the role of classification systems and modulation-based DNR.

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Understanding Digital Noise Reduction: Lab and Real World Outcomes

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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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