scholarly journals Interactions Between Digital Noise Reduction and Reverberation: Acoustic and Behavioral Effects

2020 ◽  
Vol 31 (01) ◽  
pp. 017-029
Author(s):  
Paul Reinhart ◽  
Pavel Zahorik ◽  
Pamela Souza
Keyword(s):  
Noise Reduction ◽  
Hearing Impairment ◽  
Hearing Aids ◽  
Background Noise ◽  
Speech Intelligibility ◽  
Spectral Subtraction ◽  
Listening Effort ◽  
Speech In Noise ◽  
Reverberant Environments ◽  
Speech Naturalness

AbstractDigital noise reduction (DNR) processing is used in hearing aids to enhance perception in noise by classifying and suppressing the noise acoustics. However, the efficacy of DNR processing is not known under reverberant conditions where the speech-in-noise acoustics are further degraded by reverberation.The purpose of this study was to investigate acoustic and perceptual effects of DNR processing across a range of reverberant conditions for individuals with hearing impairment.This study used an experimental design to investigate the effects of varying reverberation on speech-in-noise processed with DNR.Twenty-six listeners with mild-to-moderate sensorineural hearing impairment participated in the study.Speech stimuli were combined with unmodulated broadband noise at several signal-to-noise ratios (SNRs). A range of reverberant conditions with realistic parameters were simulated, as well as an anechoic control condition without reverberation. Reverberant speech-in-noise signals were processed using a spectral subtraction DNR simulation. Signals were acoustically analyzed using a phase inversion technique to quantify improvement in SNR as a result of DNR processing. Sentence intelligibility and subjective ratings of listening effort, speech naturalness, and background noise comfort were examined with and without DNR processing across the conditions.Improvement in SNR was greatest in the anechoic control condition and decreased as the ratio of direct to reverberant energy decreased. There was no significant effect of DNR processing on speech intelligibility in the anechoic control condition, but there was a significant decrease in speech intelligibility with DNR processing in all of the reverberant conditions. Subjectively, listeners reported greater listening effort and lower speech naturalness with DNR processing in some of the reverberant conditions. Listeners reported higher background noise comfort with DNR processing only in the anechoic control condition.Results suggest that reverberation affects DNR processing using a spectral subtraction algorithm in such a way that decreases the ability of DNR to reduce noise without distorting the speech acoustics. Overall, DNR processing may be most beneficial in environments with little reverberation and that the use of DNR processing in highly reverberant environments may actually produce adverse perceptual effects. Further research is warranted using commercial hearing aids in realistic reverberant environments.

Performance Improvement of Digital Hearing Aid Systems

2015 ◽  
Vol 1 ◽  
pp. 27
Author(s):  
Isiaka Ajewale Alimi
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Digital Signal Processor ◽  
Speech Intelligibility ◽  
Communication Systems ◽  
Hearing Aid ◽  
Spectral Subtraction ◽  
Residual Spectrum ◽  
Digital Hearing Aids ◽  
Digital Hearing Aid

Digital hearing aids addresses the issues of noise and speech intelligibility that is associated with the analogue types. One of the main functions of the digital signal processor (DSP) of digital hearing aid systems is noise reduction which can be achieved by speech enhancement algorithms which in turn improve system performance and flexibility. However, studies have shown that the quality of experience (QoE) with some of the current hearing aids is not up to expectation in a noisy environment due to interfering sound, background noise and reverberation. It is also suggested that noise reduction features of the DSP can be further improved accordingly. Recently, we proposed an adaptive spectral subtraction algorithm to enhance the performance of communication systems and address the issue of associated musical noise generated by the conventional spectral subtraction algorithm. The effectiveness of the algorithm has been confirmed by different objective and subjective evaluations. In this study, an adaptive spectral subtraction algorithm is implemented using the noise-estimation algorithm for highly non-stationary noisy environments instead of the voice activity detection (VAD) employed in our previous work due to its effectiveness. Also, signal to residual spectrum ratio (SR) is implemented in order to control the amplification distortion for speech intelligibility improvement. The results show that the proposed scheme gives comparatively better performance and can be easily employed in digital hearing aid system for improving speech quality and intelligibility.

scholarly journals Measuring the Influence of Noise Reduction on Listening Effort in Hearing-Impaired Listeners Using Response Times to an Arithmetic Task in Noise

2021 ◽  
Vol 25 ◽  
pp. 233121652110144
Author(s):  
Ilja Reinten ◽  
Inge De Ronde-Brons ◽  
Rolph Houben ◽  
Wouter Dreschler
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Speech Intelligibility ◽  
Response Times ◽  
Hearing Impaired ◽  
Listening Effort ◽  
Arithmetic Task ◽  
Test Retest Reliability ◽  
Influence Of Noise ◽  
Measured Response

Single microphone noise reduction (NR) in hearing aids can provide a subjective benefit even when there is no objective improvement in speech intelligibility. A possible explanation lies in a reduction of listening effort. Previously, we showed that response times (a proxy for listening effort) to an auditory-only dual-task were reduced by NR in normal-hearing (NH) listeners. In this study, we investigate if the results from NH listeners extend to the hearing-impaired (HI), the target group for hearing aids. In addition, we assess the relevance of the outcome measure for studying and understanding listening effort. Twelve HI subjects were asked to sum two digits of a digit triplet in noise. We measured response times to this task, as well as subjective listening effort and speech intelligibility. Stimuli were presented at three signal-to-noise ratios (SNR; –5, 0, +5 dB) and in quiet. Stimuli were processed with ideal or nonideal NR, or unprocessed. The effect of NR on response times in HI listeners was significant only in conditions where speech intelligibility was also affected (–5 dB SNR). This is in contrast to the previous results with NH listeners. There was a significant effect of SNR on response times for HI listeners. The response time measure was reasonably correlated ( R142 = 0.54) to subjective listening effort and showed a sufficient test–retest reliability. This study thus presents an objective, valid, and reliable measure for evaluating an aspect of listening effort of HI listeners.

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.

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.

scholarly journals Binaural Signal Processing in Hearing Aids

2021 ◽  
Vol 42 (03) ◽  
pp. 206-223
Author(s):  
Peter Derleth ◽  
Eleftheria Georganti ◽  
Matthias Latzel ◽  
Gilles Courtois ◽  
Markus Hofbauer ◽  
...  
Keyword(s):  
Signal Processing ◽  
Hearing Impairment ◽  
Hearing Aids ◽  
Speech Intelligibility ◽  
Sound Quality ◽  
The Body ◽  
Technological Advances ◽  
Reverberant Environments ◽  
Left And Right ◽  
Algorithmic Approaches

AbstractFor many years, clinicians have understood the advantages of listening with two ears compared with one. In addition to improved speech intelligibility in quiet, noisy, and reverberant environments, binaural versus monaural listening improves perceived sound quality and decreases the effort listeners must expend to understand a target voice of interest or to monitor a multitude of potential target voices. For most individuals with bilateral hearing impairment, the body of evidence collected across decades of research has also found that the provision of two compared with one hearing aid yields significant benefit for the user. This article briefly summarizes the major advantages of binaural compared with monaural hearing, followed by a detailed description of the related technological advances in modern hearing aids. Aspects related to the communication and exchange of data between the left and right hearing aids are discussed together with typical algorithmic approaches implemented in modern hearing aids.

The Effects of Hearing Aid Directional Microphone and Noise Reduction Processing on Listening Effort in Older Adults with Hearing Loss

2016 ◽  
Vol 27 (01) ◽  
pp. 029-041 ◽  
Author(s):  
Jamie L. Desjardins
Keyword(s):  
Hearing Loss ◽  
Cognitive Load ◽  
Noise Reduction ◽  
Cognitive Processing ◽  
Hearing Aids ◽  
Background Noise ◽  
Hearing Aid ◽  
Recognition Performance ◽  
Listening Effort ◽  
Directional Microphone

Background: Older listeners with hearing loss may exert more cognitive resources to maintain a level of listening performance similar to that of younger listeners with normal hearing. Unfortunately, this increase in cognitive load, which is often conceptualized as increased listening effort, may come at the cost of cognitive processing resources that might otherwise be available for other tasks. Purpose: The purpose of this study was to evaluate the independent and combined effects of a hearing aid directional microphone and a noise reduction (NR) algorithm on reducing the listening effort older listeners with hearing loss expend on a speech-in-noise task. Research Design: Participants were fitted with study worn commercially available behind-the-ear hearing aids. Listening effort on a sentence recognition in noise task was measured using an objective auditory–visual dual-task paradigm. The primary task required participants to repeat sentences presented in quiet and in a four-talker babble. The secondary task was a digital visual pursuit rotor-tracking test, for which participants were instructed to use a computer mouse to track a moving target around an ellipse that was displayed on a computer screen. Each of the two tasks was presented separately and concurrently at a fixed overall speech recognition performance level of 50% correct with and without the directional microphone and/or the NR algorithm activated in the hearing aids. In addition, participants reported how effortful it was to listen to the sentences in quiet and in background noise in the different hearing aid listening conditions. Study Sample: Fifteen older listeners with mild sloping to severe sensorineural hearing loss participated in this study. Results: Listening effort in background noise was significantly reduced with the directional microphones activated in the hearing aids. However, there was no significant change in listening effort with the hearing aid NR algorithm compared to no noise processing. Correlation analysis between objective and self-reported ratings of listening effort showed no significant relation. Conclusions: Directional microphone processing effectively reduced the cognitive load of listening to speech in background noise. This is significant because it is likely that listeners with hearing impairment will frequently encounter noisy speech in their everyday communications.

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.

scholarly journals Multitasking with typical use of hearing aid noise reduction in older listeners

2018 ◽  
Author(s):  
Tim Schoof ◽  
Pamela Souza
Keyword(s):  
Speech Recognition ◽  
Noise Reduction ◽  
Hearing Aids ◽  
Recognition Task ◽  
Hearing Impaired ◽  
Improve Performance ◽  
Sentence Recognition ◽  
Monitoring Task ◽  
Speech In Noise ◽  
Dual Task Paradigm

Objective: Older hearing-impaired adults typically experience difficulties understanding speech in noise. Most hearing aids address this issue using digital noise reduction. While noise reduction does not necessarily improve speech recognition, it may reduce the resources required to process the speech signal. Those available resources may, in turn, aid the ability to perform another task while listening to speech (i.e., multitasking). This study examined to what extent changing the strength of digital noise reduction in hearing aids affects the ability to multitask. Design: Multitasking was measured using a dual-task paradigm, combining a speech recognition task and a visual monitoring task. The speech recognition task involved sentence recognition in the presence of six-talker babble at signal-to-noise ratios (SNRs) of 2 and 7 dB. Participants were fit with commercially-available hearing aids programmed under three noise reduction settings: off, mild, strong. Study sample: 18 hearing-impaired older adults. Results: There were no effects of noise reduction on the ability to multitask, or on the ability to recognize speech in noise. Conclusions: Adjustment of noise reduction settings in the clinic may not invariably improve performance for some tasks.

Evaluation of a Transient Noise Reduction Strategy for Hearing Aids

2012 ◽  
Vol 23 (08) ◽  
pp. 606-615 ◽  
Author(s):  
HaiHong Liu ◽  
Hua Zhang ◽  
Ruth A. Bentler ◽  
Demin Han ◽  
Luo Zhang
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Background Noise ◽  
Hearing Aid ◽  
Sound Quality ◽  
T Test ◽  
Reduction Strategy ◽  
Speech Stimuli ◽  
Significant Difference ◽  
Speech Clarity

Background: Transient noise can be disruptive for people wearing hearing aids. Ideally, the transient noise should be detected and controlled by the signal processor without disrupting speech and other intended input signals. A technology for detecting and controlling transient noises in hearing aids was evaluated in this study. Purpose: The purpose of this study was to evaluate the effectiveness of a transient noise reduction strategy on various transient noises and to determine whether the strategy has a negative impact on sound quality of intended speech inputs. Research Design: This was a quasi-experimental study. The study involved 24 hearing aid users. Each participant was asked to rate the parameters of speech clarity, transient noise loudness, and overall impression for speech stimuli under the algorithm-on and algorithm-off conditions. During the evaluation, three types of stimuli were used: transient noises, speech, and background noises. The transient noises included “knife on a ceramic board,” “mug on a tabletop,” “office door slamming,” “car door slamming,” and “pen tapping on countertop.” The speech sentences used for the test were presented by a male speaker in Mandarin. The background noises included “party noise” and “traffic noise.” All of these sounds were combined into five listening situations: (1) speech only, (2) transient noise only, (3) speech and transient noise, (4) background noise and transient noise, and (5) speech and background noise and transient noise. Results: There was no significant difference on the ratings of speech clarity between the algorithm-on and algorithm-off (t-test, p = 0.103). Further analysis revealed that speech clarity was significant better at 70 dB SLP than 55 dB SPL (p < 0.001). For transient noise loudness: under the algorithm-off condition, the percentages of subjects rating the transient noise to be somewhat soft, appropriate, somewhat loud, and too loud were 0.2, 47.1, 29.6, and 23.1%, respectively. The corresponding percentages under the algorithm-on were 3.0, 72.6, 22.9, and 1.4%, respectively. A significant difference on the ratings of the transient noise loudness was found between the algorithm-on and algorithm-off (t-test, p < 0.001). For overall impression for speech stimuli: under the algorithm-off condition, the percentage of subjects rating the algorithm to be not helpful at all, somewhat helpful, helpful, and very helpful for speech stimuli were 36.5, 20.8, 33.9, and 8.9%, respectively. Under the algorithm-on condition, the corresponding percentages were 35.0, 19.3, 30.7, and 15.0%, respectively. Statistical analysis revealed there was a significant difference on the ratings of overall impression on speech stimuli. The ratings under the algorithm-on condition were significantly more helpful for speech understanding than the ratings under algorithm-off (t-test, p < 0.001). Conclusions: The transient noise reduction strategy appropriately controlled the loudness for most of the transient noises and did not affect the sound quality, which could be beneficial to hearing aid wearers.

scholarly journals Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures

2019 ◽  
Vol 23 ◽  
pp. 233121651984829 ◽  
Author(s):  
Ghada BinKhamis ◽  
Antonio Elia Forte ◽  
Tobias Reichenbach ◽  
Martin O’Driscoll ◽  
Karolina Kluk
Keyword(s):  
Hearing Aids ◽  
Background Noise ◽  
Objective Measure ◽  
Auditory Brainstem ◽  
Self Report ◽  
Auditory Brainstem Responses ◽  
Speech Understanding ◽  
Reported Speech ◽  
Speech Detection ◽  
Speech In Noise

Evaluation of patients who are unable to provide behavioral responses on standard clinical measures is challenging due to the lack of standard objective (non-behavioral) clinical audiological measures that assess the outcome of an intervention (e.g., hearing aids). Brainstem responses to short consonant-vowel stimuli (speech-auditory brainstem responses [speech-ABRs]) have been proposed as a measure of subcortical encoding of speech, speech detection, and speech-in-noise performance in individuals with normal hearing. Here, we investigated the potential application of speech-ABRs as an objective clinical outcome measure of speech detection, speech-in-noise detection and recognition, and self-reported speech understanding in 98 adults with sensorineural hearing loss. We compared aided and unaided speech-ABRs, and speech-ABRs in quiet and in noise. In addition, we evaluated whether speech-ABR F0 encoding (obtained from the complex cross-correlation with the 40 ms [da] fundamental waveform) predicted aided behavioral speech recognition in noise or aided self-reported speech understanding. Results showed that (a) aided speech-ABRs had earlier peak latencies, larger peak amplitudes, and larger F0 encoding amplitudes compared to unaided speech-ABRs; (b) the addition of background noise resulted in later F0 encoding latencies but did not have an effect on peak latencies and amplitudes or on F0 encoding amplitudes; and (c) speech-ABRs were not a significant predictor of any of the behavioral or self-report measures. These results show that speech-ABR F0 encoding is not a good predictor of speech-in-noise recognition or self-reported speech understanding with hearing aids. However, our results suggest that speech-ABRs may have potential for clinical application as an objective measure of speech detection with hearing aids.

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