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 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.

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 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.

Encoding of speech in noise in adults using hearing aids: effect of noise reduction algorithm

2019 ◽  
Vol 18 (2) ◽  
pp. 98-104
Author(s):  
Hiba Ahmed El-Assal ◽  
Amani Mohamed El-Gharib ◽  
Enaas Ahmad Kolkaila ◽  
Trandil Hassan Elmahallawy
Keyword(s):  
Noise Reduction ◽  
Hearing Aids ◽  
Reduction Algorithm ◽  
Speech In Noise ◽  
Noise Reduction Algorithm

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 Feasibility of Total Variation Noise Reduction Algorithm According to Various MR-Based PET Images in a Simultaneous PET/MR System: A Phantom Study

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 319
Author(s):  
Chan-Rok Park ◽  
Seong-Hyeon Kang ◽  
Young-Jin Lee
Keyword(s):  
Image Quality ◽  
Noise Reduction ◽  
Total Variation ◽  
Parallel Imaging ◽  
Pulse Sequence ◽  
Reduction Algorithm ◽  
Median Filters ◽  
Reduction Techniques ◽  
Imaging Results ◽  
Noise Reduction Algorithm

Recently, the total variation (TV) algorithm has been used for noise reduction distribution in degraded nuclear medicine images. To acquire positron emission tomography (PET) to correct the attenuation region in the PET/magnetic resonance (MR) system, the MR Dixon pulse sequence, which is based on controlled aliasing in parallel imaging, results from higher acceleration (CAIPI; MR-ACDixon-CAIPI) and generalized autocalibrating partially parallel acquisition (GRAPPA; MR-ACDixon-GRAPPA) algorithms are used. Therefore, this study aimed to evaluate the image performance of the TV noise reduction algorithm for PET/MR images using the Jaszczak phantom by injecting 18F radioisotopes with PET/MR, which is called mMR (Siemens, Germany), compared with conventional noise-reduction techniques such as Wiener and median filters. The contrast-to-noise (CNR) and coefficient of variation (COV) were used for quantitative analysis. Based on the results, PET images with the TV algorithm were improved by approximately 7.6% for CNR and decreased by approximately 20.0% for COV compared with conventional noise-reduction techniques. In particular, the image quality for the MR-ACDixon-CAIPI PET image was better than that of the MR-ACDixon-GRAPPA PET image. In conclusion, the TV noise-reduction algorithm is efficient for improving the PET image quality in PET/MR systems.

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