Effects of a Transient Noise Reduction Algorithm on Speech Understanding, Subjective Preference, and Preferred Gain

2013 ◽  
Vol 24 (09) ◽  
pp. 845-858 ◽  
Author(s):  
Petri Korhonen ◽  
Francis Kuk ◽  
Chi Lau ◽  
Denise Keenan ◽  
Jennifer Schumacher ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Repeated Measures ◽  
Speech Intelligibility ◽  
Repeated Measures Design ◽  
Subjective Preference ◽  
Listening Environments ◽  
Speech Identification ◽  
Noise Reduction Algorithm ◽  
Phoneme Identification

Background: Today's compression hearing aids with noise reduction systems may not manage transient noises effectively because of the short duration of these sounds compared to the onset times of the compressors and/or noise reduction algorithms. Purpose: The current study was designed to evaluate the effect of a transient noise reduction (TNR) algorithm on listening comfort, speech intelligibility in quiet, and preferred wearer gain in the presence of transients. Research Design: A single-blinded, repeated-measures design was used. Study Sample: Thirteen experienced hearing aid users with bilaterally symmetrical (≤7.5 dB) sensorineural hearing loss participated in the study. Results: Speech identification in quiet (no transient noise) was identical between the TNR On and the TNR Off conditions. The participants showed subjective preference for the TNR algorithm when “comfortable listening” was used as the criterion. Participants preferred less gain than the default prescription in the presence of transient noise sounds. However, the preferred gain was 2.9 dB higher when the TNR was activated than when it was deactivated. This translated to 12.1% improvement in phoneme identification over the TNR Off condition for soft speech. Conclusions: This study demonstrated that the use of the TNR algorithm would not negatively affect speech identification. The results also suggested that this algorithm may improve listening comfort in the presence of transient noise sounds and ensure consistent use of prescribed gain. Such an algorithm may ensure more consistent audibility across listening environments.

Speech Intelligibility Benefits of Hearing Aids at Various Input Levels

2015 ◽  
Vol 26 (03) ◽  
pp. 275-288 ◽  
Author(s):  
Francis Kuk ◽  
Chi-chuen Lau ◽  
Petri Korhonen ◽  
Bryan Crose
Keyword(s):  
Speech Perception ◽  
Hearing Aids ◽  
Repeated Measures ◽  
Speech Intelligibility ◽  
Digital Signal ◽  
High Noise ◽  
Repeated Measures Design ◽  
Speech In Noise ◽  
Directional Microphone ◽  
Subjective Preference

Background: Although the benefits of hearing aids are generally recognized for soft- and conversational-level sounds, most studies have reported negative benefits (i.e., poorer aided than unaided performance) at high noise inputs. Advances in digital signal processing such as compression, noise reduction, and directional microphone could improve speech perception at high input levels. This could alter our view on the efficacy of hearing aids in loud, noisy situations. Purpose: The current study compared the aided versus the unaided speech intelligibility performance of hearing-impaired (HI) listeners at various input levels (from 50–100 dB SPL) and signal-to-noise ratios (SNRs; quiet, +6, +3, and –3 dB) in order to document the benefits of modern hearing aids. In addition, subjective preference between aided and unaided sounds (speech and music) at various input levels was also compared. Research Design: The experiment used a factorial repeated-measures design. Study Sample: A total of 10 HI adults with symmetrical moderate to severe hearing losses served as test participants. In addition, speech intelligibility scores of five normal-hearing (NH) listeners were also measured for comparison. Intervention: Speech perception was studied at 50 and 65 dB SPL input levels in quiet and also in noise at levels of 65, 85, and 100 dB SPL with SNRs of +6, +3, and –3 dB. This was done for all participants (HI and NH). In addition, the HI participants compared subjective preference between the aided and unaided presentations of speech and music stimuli at 50, 65, 85, and 100 dB SPL in quiet. Data Collection and Analysis: The data were analyzed with repeated-measures analysis of variance. Results: The results showed a decrease in aided benefits as input levels increased. However, even at the two highest input levels (i.e., 85 and 100 dB SPL), aided speech scores were still higher than the unaided speech scores. Furthermore, NH listeners and HI listeners in the aided condition showed stable speech-in-noise performance between 65 and 100 dB SPL input levels, except that the absolute performance of the NH listeners was higher than that of the HI listeners. Subjective preference for the unaided sounds versus the aided sounds increased as input level increased, with a crossover intensity at approximately 75 dB SPL for speech and 80 dB SPL for music. Conclusions: The results supported the hypothesis that the study hearing aid can provide aided speech-in-noise benefit at very high noise inputs in a controlled environment.

scholarly journals A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features

2015 ◽  
Vol 471 (2184) ◽  
pp. 20150309 ◽  
Author(s):  
Tyler Lee ◽  
Frédéric Theunissen
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Background Noise ◽  
Speech Intelligibility ◽  
State Of The Art ◽  
Reduction Algorithm ◽  
Temporal Correlations ◽  
Temporal Features ◽  
Wide Range ◽  
Noise Reduction Algorithm

Animals throughout the animal kingdom excel at extracting individual sounds from competing background sounds, yet current state-of-the-art signal processing algorithms struggle to process speech in the presence of even modest background noise. Recent psychophysical experiments in humans and electrophysiological recordings in animal models suggest that the brain is adapted to process sounds within the restricted domain of spectro-temporal modulations found in natural sounds. Here, we describe a novel single microphone noise reduction algorithm called spectro-temporal detection–reconstruction (STDR) that relies on an artificial neural network trained to detect, extract and reconstruct the spectro-temporal features found in speech. STDR can significantly reduce the level of the background noise while preserving the foreground speech quality and improving estimates of speech intelligibility. In addition, by leveraging the strong temporal correlations present in speech, the STDR algorithm can also operate on predictions of upcoming speech features, retaining similar performance levels while minimizing inherent throughput delays. STDR performs better than a competing state-of-the-art algorithm for a wide range of signal-to-noise ratios and has the potential for real-time applications such as hearing aids and automatic speech recognition.

The Effects of Noise Reduction Technologies on the Acceptance of Background Noise

2013 ◽  
Vol 24 (08) ◽  
pp. 649-659 ◽  
Author(s):  
Kristy Jones Lowery ◽  
Patrick N. Plyler
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Repeated Measures ◽  
Background Noise ◽  
Negative Effects ◽  
Directional Microphones ◽  
Repeated Measures Design ◽  
Noise Type ◽  
Data Collection And Analysis ◽  
Speech Spectrum

Background: Directional microphones (D-Mics) and digital noise reduction (DNR) algorithms are used in hearing aids to reduce the negative effects of background noise on performance. Directional microphones attenuate sounds arriving from anywhere other than the front of the listener while DNR attenuates sounds with physical characteristics of noise. Although both noise reduction technologies are currently available in hearing aids, it is unclear if the use of these technologies in isolation or together affects acceptance of noise and/or preference for the end user when used in various types of background noise. Purpose: The purpose of the research was to determine the effects of D-Mic, DNR, or the combination of D-Mic and DNR on acceptance of noise and preference when listening in various types of background noise. Research Design: An experimental study in which subjects were exposed to a repeated measures design was utilized. Study Sample: Thirty adult listeners with mild sloping to moderately severe sensorineural hearing loss participated (mean age 67 yr). Data Collection and Analysis: Acceptable noise levels (ANLs) were obtained using no noise reduction technologies, D-Mic only, DNR only, and the combination of the two technologies (Combo) for three different background noises (single-talker speech, speech-shaped noise, and multitalker babble) for each listener. In addition, preference rankings of the noise reduction technologies were obtained within each background noise (1 = best, 3 = worst). Results: ANL values were significantly better for each noise reduction technology than baseline; and benefit increased significantly from DNR to D-Mic to Combo. Listeners with higher (worse) baseline ANLs received more benefit from noise reduction technologies than listeners with lower (better) baseline ANLs. Neither ANL values nor ANL benefit values were significantly affected by background noise type; however, ANL benefit with D-Mic and Combo was similar when speech-like noise was present while ANL benefit was greatest for Combo when speech spectrum noise was present. Listeners preferred the hearing aid settings that resulted in the best ANL value. Conclusion: Noise reduction technologies improved ANL for each noise type, and the amount of improvement was related to the baseline ANL value. Improving an ANL with noise reduction technologies is noticeable to listeners, at least when examined in this laboratory setting, and listeners prefer noise reduction technologies that improved their ability to accept noise.

scholarly journals Noise reduction in hearing aids using spectral subtraction techniques

2021 ◽  
Author(s):  
Fatos Myftari
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Speech Intelligibility ◽  
Single Channel ◽  
Subjective Evaluation ◽  
Hearing Impaired ◽  
Spectral Subtraction ◽  
Reduction Algorithm ◽  
Subjective Testing ◽  
Noise Reduction Algorithm

This thesis is concerned with noise reduction in hearing aids. Hearing - impaired listeners and hearing - impaired users have great difficulty understanding speech in a noisy background. This problem has motivated the development and the use of noise reduction algorithms to improve the speech intelligibility in hearing aids. In this thesis, two noise reduction algorithms for single channel hearing instruments are presented, evaluated using objective and subjective tests. The first noise reduction algorithm, conventional Spectral Subtraction, is simulated using MATLAB 6.5, R13. The second noise reduction algorithm, Spectral Subtraction in wavelet domanin is introduced as well. This algorithm is implemented off line, and is compared with conventional Spectral Subtraction. A subjective evaluation demonstrates that the second algorithm has additional advantages in speech intelligibility, in poor listening conditions relative to conventional Spectral Subtraction. The subjective testing was performed with normal hearing listeners, at Ryerson University. The objective evaluation shows that the Spectral Subtraction in wavelet domain has improved Signal to Noise Ratio compared to conventional Spectral Subtraction.

Evaluation of a Wind Noise Attenuation Algorithm on Subjective Annoyance and Speech-in-Wind Performance

2017 ◽  
Vol 28 (01) ◽  
pp. 046-057 ◽  
Author(s):  
Petri Korhonen ◽  
Francis Kuk ◽  
Eric Seper ◽  
Martin Mørkebjerg ◽  
Majken Roikjer
Keyword(s):  
Hearing Aids ◽  
Repeated Measures ◽  
Hearing Aid ◽  
Real Life ◽  
Subjective Rating ◽  
Noise Attenuation ◽  
Wind Noise ◽  
Wind Speeds ◽  
Repeated Measures Design ◽  
Phoneme Identification

AbstractWind noise is a common problem reported by hearing aid wearers. The MarkeTrak VIII reported that 42% of hearing aid wearers are not satisfied with the performance of their hearing aids in situations where wind is present.The current study investigated the effect of a new wind noise attenuation (WNA) algorithm on subjective annoyance and speech recognition in the presence of wind.A single-blinded, repeated measures design was used.Fifteen experienced hearing aid wearers with bilaterally symmetrical (≤10 dB) mild-to-moderate sensorineural hearing loss participated in the study.Subjective rating for wind noise annoyance was measured for wind presented alone from 0° and 290° at wind speeds of 4, 5, 6, 7, and 10 m/sec. Phoneme identification performance was measured using Widex Office of Clinical Amplification Nonsense Syllable Test presented at 60, 65, 70, and 75 dB SPL from 270° in the presence of wind originating from 0° at a speed of 5 m/sec.The subjective annoyance from wind noise was reduced for wind originating from 0° at wind speeds from 4 to 7 m/sec. The largest improvement in phoneme identification with the WNA algorithm was 48.2% when speech was presented from 270° at 65 dB SPL and the wind originated from 0° azimuth at 5 m/sec.The WNA algorithm used in this study reduced subjective annoyance for wind speeds ranging from 4 to 7 m/sec. The algorithm was effective in improving speech identification in the presence of wind originating from 0° at 5 m/sec. These results suggest that the WNA algorithm used in the current study could expand the range of real-life situations where a hearing-impaired person can use the hearing aid optimally.

Effect of Maximum Power Output and Noise Reduction on Speech Recognition in Noise

2011 ◽  
Vol 22 (05) ◽  
pp. 265-273 ◽  
Author(s):  
Francis Kuk ◽  
Heidi Peeters ◽  
Chi Lau ◽  
Petri Korhonen
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Power Output ◽  
Noise Level ◽  
Repeated Measures ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Maximum Power ◽  
Maximum Power Output ◽  
Bonferroni Adjustment

Background: The maximum power output (MPO) of a hearing aid was typically discussed in the context of avoiding loudness discomfort. However, an MPO that is too low, as in the cases to avoid discomfort for people with a severe loudness tolerance problem and hearing losses that exceed the fitting range of the hearing aids, could negatively affect sound quality and speech intelligibility in noise. Purpose: The current study was designed to demonstrate the degradation in speech intelligibility in noise on the HINT (Hearing in Noise Test) when the MPO of the wearers' hearing aids was lowered by 10 dB from the default. The interactions with noise reduction (NR) algorithms (classic [NR-classic] and Speech Enhancer [NR-SE]) were also examined. Research Design: A single-blinded, factorial repeated-measures design was used to study the effect of noise input level (68 dBC, 75 dBC), MPO setting (default and default-10), and NR algorithm (off, classic, SE) on HINT performance. Study Sample: Eleven adults with a severe sensorineural hearing loss participated. Intervention: Participants were fit with the Widex m4-19 behind-the-ear hearing aids binaurally in the default frequency response and MPO settings. The hearing aids were adjusted to six MPO (default, default-10) by NR (off, classic, SE conditions). Testing was completed within one 2 hr session. Data Collection and Analysis: The RTS (reception threshold for speech) for 50% correct on the HINT was measured in each of the six hearing aid conditions at two input levels (68 and 75 dBC) with speech and noise stimuli presented from the front. Repeated-measures ANOVAs were conducted using SPSS software to examine significant differences. Results: A repeated-measures ANOVA showed that noise level was not significant while NR algorithm and MPO were significant. The interaction between noise level and NR algorithm was also significant. Post hoc analysis with Bonferroni adjustment for the effect of NR algorithm showed that performance with NR-off was significantly poorer than performance with NR-classic and NR-SE (p < 0.05). However, NR-classic and NR-SE were not significantly different from each other (p > 0.05). Conclusions: An MPO that was 10 dB lower than the default could negatively affect the signal-to-noise ratio (SNR) of the listening environment. However, NR could compensate for the degradation in SNR.

Using the Repeat-Recall Test to Examine Factors Affecting Context Use

2021 ◽  
Author(s):  
Francis Kuk ◽  
Christopher Slugocki ◽  
Petri Korhonen
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Speech Processing ◽  
Repeated Measures ◽  
Recall Test ◽  
Outcome Measure ◽  
Hearing Aid ◽  
Listening Effort ◽  
Double Blind ◽  
Repeated Measures Design

Abstract Background The effect of context on speech processing has been studied using different speech materials and response criteria. The Repeat-Recall Test (RRT) evaluates listener performance using high context (HC) and low context (LC) sentences; this may offer another platform for studying context use (CU). Objective This article aims to evaluate if the RRT may be used to study how different signal-to-noise ratios (SNRs), hearing aid technologies (directional microphone and noise reduction), and listener working memory capacities (WMCs) interact to affect CU on the different measures of the RRT. Design Double-blind, within-subject repeated measures design. Study Sample Nineteen listeners with a mild-to-moderately severe hearing loss. Data Collection The RRT was administered with participants wearing the study hearing aids under two microphone (omnidirectional vs. directional) by two noise reduction (on vs. off) conditions. Speech was presented from 0 degree at 75 dB sound pressure level and a continuous speech-shaped noise from 180 degrees at SNRs of 0, 5, 10, and 15 dB. The order of SNR and hearing aid conditions was counterbalanced across listeners. Each test condition was completed twice in two 2-hour sessions separated by 1 month. Results CU was calculated as the difference between HC and LC sentence scores for each outcome measure (i.e., repeat, recall, listening effort, and tolerable time). For all outcome measures, repeated measures analyses of variance revealed that CU was significantly affected by the SNR of the test conditions. For repeat, recall, and listening effort measures, these effects were qualified by significant two-way interactions between SNR and microphone mode. In addition, the WMC group significantly affected CU during recall and rating of listening effort, the latter of which was qualified by an interaction between the WMC group and SNR. Listener WMC affected CU on estimates of tolerable time as qualified by significant two-way interactions between SNR and microphone mode. Conclusion The study supports use of the RRT as a tool for measuring how listeners use sentence context to aid in speech processing. The degree to which context influenced scores on each outcome measure of the RRT was found to depend on complex interactions between the SNR of the listening environment, hearing aid features, and the WMC of the listeners.

scholarly journals An Integrative Evaluation of the Efficacy of a Directional Microphone and Noise-Reduction Algorithm under Realistic Signal-to-Noise Ratios

2020 ◽  
Vol 31 (04) ◽  
pp. 262-270
Author(s):  
Francis Kuk ◽  
Christopher Slugocki ◽  
Petri Korhonen
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Repeated Measures ◽  
Signal To Noise Ratio ◽  
Working Memory Capacity ◽  
Moderate Degree ◽  
Signal To Noise ◽  
Listening Effort ◽  
Double Blind ◽  
Repeated Measures Design

Abstract Background Many studies on the efficacy of directional microphones (DIRMs) and noise-reduction (NR) algorithms were not conducted under realistic signal-to-noise ratio (SNR) conditions. A Repeat-Recall Test (RRT) was developed previously to partially address this issue. Purpose This study evaluated whether the RRT could provide a more comprehensive understanding of the efficacy of a DIRM and NR algorithm under realistic SNRs. Possible interaction with listener working memory capacity (WMC) was assessed. Research Design This study uses a double-blind, within-subject repeated measures design. Study Sample Nineteen listeners with a moderate degree of hearing loss participated. Data Collection and Analysis The RRT was administered with participants wearing the study hearing aids (HAs) under two microphones (omnidirectional versus directional) by two NR (on versus off) conditions. Speech was presented from 0° at 75 dB SPL and a continuous noise from 180° at SNRs of 0, 5, 10, and 15 dB. The order of SNR and HA conditions was counterbalanced across listeners. Each test condition was completed twice in two 2-hour sessions separated by one month. Results The recall scores of listeners were used to group listeners into good and poor WMC groups. Analysis using linear mixed-effects models revealed significant effects of context, SNR, and microphone for all four measures (repeat, recall, listening effort, and tolerable time). NR was only significant on the listening effort scale in the DIRM mode at an SNR of 5 dB. Listeners with good WMC performed better on all measures of the RRT and benefitted more from context. Although DIRM benefitted listeners with good and poor WMC, the benefits differed by context and SNR. Conclusions The RRT confirmed the efficacy of DIRM and NR on several outcome measures under realistic SNRs. It also highlighted interactions between WMC and sentence context on feature efficacy.

Subjective and Objective Evaluation of Noise Management Algorithms

2009 ◽  
Vol 20 (02) ◽  
pp. 089-098 ◽  
Author(s):  
Heidi Peeters ◽  
Francis Kuk ◽  
Chi-chuen Lau ◽  
Denise Keenan
Keyword(s):  
Noise Reduction ◽  
Speech Intelligibility ◽  
Objective Evaluation ◽  
Reduction Algorithm ◽  
Speech In Noise ◽  
Directional Microphone ◽  
Hearing In Noise ◽  
Listening Environments ◽  
Speech Intelligibility Index ◽  
Noise Reduction Algorithm

Purpose: To measure the subjective and objective improvement of speech intelligibility in noise offered by a commercial hearing aid that uses a fully adaptive directional microphone and a noise reduction algorithm that optimizes the Speech Intelligibility Index (SII). Research Design: Comparison of results on the Hearing in Noise Test (HINT) and the Acceptable Noise Level task (ANL). Study Sample: Eighteen participants with varying configurations of sensorineural hearing loss. Results: Both the directional microphone and the noise reduction algorithm improved the speech-in-noise performance of the participants. The benefits reported were higher for the directional microphone than the noise reduction algorithm. A moderate correlation was noted between the benefits measured on the HINT and the ANL for the directional microphone condition, the noise reduction condition, and the directional microphone plus noise reduction conditions. Conclusions: These results suggest that the directional microphone and the SII-based noise reduction algorithm may improve the SNR of the listening environments, and both the HINT and the ANL may be used to study their benefits.

scholarly journals Noise reduction in hearing aids using spectral subtraction techniques

2021 ◽  
Author(s):  
Fatos Myftari
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Speech Intelligibility ◽  
Single Channel ◽  
Subjective Evaluation ◽  
Hearing Impaired ◽  
Spectral Subtraction ◽  
Reduction Algorithm ◽  
Subjective Testing ◽  
Noise Reduction Algorithm

This thesis is concerned with noise reduction in hearing aids. Hearing - impaired listeners and hearing - impaired users have great difficulty understanding speech in a noisy background. This problem has motivated the development and the use of noise reduction algorithms to improve the speech intelligibility in hearing aids. In this thesis, two noise reduction algorithms for single channel hearing instruments are presented, evaluated using objective and subjective tests. The first noise reduction algorithm, conventional Spectral Subtraction, is simulated using MATLAB 6.5, R13. The second noise reduction algorithm, Spectral Subtraction in wavelet domanin is introduced as well. This algorithm is implemented off line, and is compared with conventional Spectral Subtraction. A subjective evaluation demonstrates that the second algorithm has additional advantages in speech intelligibility, in poor listening conditions relative to conventional Spectral Subtraction. The subjective testing was performed with normal hearing listeners, at Ryerson University. The objective evaluation shows that the Spectral Subtraction in wavelet domain has improved Signal to Noise Ratio compared to conventional Spectral Subtraction.

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