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 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 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.

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.

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.

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.

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