1. Digital Noise Reduction: Understanding Lab and Real World Outcomes Ruth Bentler University of Iowa

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

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 Smoothing TM …  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)

11. 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

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

13. Time waveform of a random noise

14. Time waveform of a sample speech signal

15. Modulation spectra Speech Noise

16. Example of algorithm “rule #1”

17. Example of algorithm “rule #2”

25. DNR: What happens in the time domain?

26. Siemens (Triano)

27. Starkey (Axent)

28. Widex (Diva)

33. 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

34. Average RMS power: -48.04dB 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)

35. Reduction: 4.25dB Average RMS Speech= -43.79dB Average RMS Noise= -48.04dB 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)

36. 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

37. 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)

38. 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= -30.47dB Average RMS Noise= -44.03dB

39. Average RMS power: -31.96dB

40. Average RMS power: -29.01dB

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

42. Data?

43.  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

44. 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

45. 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

46. 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)

47. 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

48. 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

49. 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.

50. 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)

51. 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!

52. Is it functioning as intended?

54. 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…

55. Questions?