Rohan ambani

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the “clean” speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality/comfort. The conditions were: unprocessed noisy speech; noisy speech processed using the RNN; and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids.

Rajesh verma

Many hearing aid users are negatively impacted by wind noise when spending time outdoors. Turbulent airflow around hearing aid microphones caused by the obstruction of wind can result in noise that is not only perceived as annoying but may also mask desirable sounds in the listening environment, such as speech. To mitigate the adverse effects of wind noise, hearing aid developers have introduced several technological solutions to reduce the amount of wind noise at the hearing aid output. Some solutions are based on mechanical modifications; more recently, sophisticated signal processing algorithms have also been introduced. By offering solutions to the wind noise problem, these signal processing algorithms can promote more optimal use of hearing aids during outdoor activities. This article reviews how wind noise is generated in hearing aids, outlines the technological challenges in wind noise management, and summarizes the technological solutions that have been proposed and/or implemented in modern hearing aids.

kapil Roy

The sound quality of a turn signal sound is directly related to the driving safety and influences comfort during driving. A turn signal sound and interior noise always exist simultaneously while driving. However, none of the studies are performed to observe and control the change of a turn signal sound when the vehicle interior noise was changed. Therefore, this paper develops a method to evaluate and control the warning effect of a turn signal sound masked by vehicle interior noise. First, the turn signal sound and the vehicle interior noise at different speeds are measured and the time-frequency characteristics of the turn signal sound is analyzed. Second, the pairwise comparison method is applied to subjectively evaluate the warning effect of the sounds and the warning effect of the turn signal sound at 6 speeds relative to that at 40 km/h was obtained. Third, a new hearing threshold evaluating the warning effect is developed and used as a sound metric. This metric is employed to evaluate objectively the warning effect of warning sounds at different speeds. The subjective evaluation results are consistent with the objective evaluation results, which confirms the validity of objective evaluation. To keep the warning effect of the warning sound during the driving, two methods of controlling the turn signal sound are proposed and will be researched further in the in the future.

Prashant singh

This work presents a two-microphone speech enhancement (SE) framework based on basic recurrent neural network (RNN) cell. The proposed method operates in real-time, improving the speech quality and intelligibility in noisy environments. The RNN model trained using a simple feature set—real and imaginary parts of the short-time Fourier transform (STFT) are computationally efficient with a minimal input-output processing delay. The proposed algorithm can be used in any stand-alone platform such as a smartphone using its two inbuilt microphones. The detailed operation of the real-time implementation on the smartphone is presented. The developed application works as an assistive tool for hearing aid devices (HADs). Speech quality and intelligibility test results are used to compare the proposed algorithm to existing conventional and neural network-based SE methods. Subjective and objective scores show the superior performance of the developed method over several conventional methods in different noise conditions and low signal to noise ratios (SNRs).

Rohan ambani

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the “clean” speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality/comfort. The conditions were: unprocessed noisy speech; noisy speech processed using the RNN; and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids.

Rajesh verma

Many hearing aid users are negatively impacted by wind noise when spending time outdoors. Turbulent airflow around hearing aid microphones caused by the obstruction of wind can result in noise that is not only perceived as annoying but may also mask desirable sounds in the listening environment, such as speech. To mitigate the adverse effects of wind noise, hearing aid developers have introduced several technological solutions to reduce the amount of wind noise at the hearing aid output. Some solutions are based on mechanical modifications; more recently, sophisticated signal processing algorithms have also been introduced. By offering solutions to the wind noise problem, these signal processing algorithms can promote more optimal use of hearing aids during outdoor activities. This article reviews how wind noise is generated in hearing aids, outlines the technological challenges in wind noise management, and summarizes the technological solutions that have been proposed and/or implemented in modern hearing aids.

kapil Roy

The sound quality of a turn signal sound is directly related to the driving safety and influences comfort during driving. A turn signal sound and interior noise always exist simultaneously while driving. However, none of the studies are performed to observe and control the change of a turn signal sound when the vehicle interior noise was changed. Therefore, this paper develops a method to evaluate and control the warning effect of a turn signal sound masked by vehicle interior noise. First, the turn signal sound and the vehicle interior noise at different speeds are measured and the time-frequency characteristics of the turn signal sound is analyzed. Second, the pairwise comparison method is applied to subjectively evaluate the warning effect of the sounds and the warning effect of the turn signal sound at 6 speeds relative to that at 40 km/h was obtained. Third, a new hearing threshold evaluating the warning effect is developed and used as a sound metric. This metric is employed to evaluate objectively the warning effect of warning sounds at different speeds. The subjective evaluation results are consistent with the objective evaluation results, which confirms the validity of objective evaluation. To keep the warning effect of the warning sound during the driving, two methods of controlling the turn signal sound are proposed and will be researched further in the in the future.

Prashant singh

This work presents a two-microphone speech enhancement (SE) framework based on basic recurrent neural network (RNN) cell. The proposed method operates in real-time, improving the speech quality and intelligibility in noisy environments. The RNN model trained using a simple feature set—real and imaginary parts of the short-time Fourier transform (STFT) are computationally efficient with a minimal input-output processing delay. The proposed algorithm can be used in any stand-alone platform such as a smartphone using its two inbuilt microphones. The detailed operation of the real-time implementation on the smartphone is presented. The developed application works as an assistive tool for hearing aid devices (HADs). Speech quality and intelligibility test results are used to compare the proposed algorithm to existing conventional and neural network-based SE methods. Subjective and objective scores show the superior performance of the developed method over several conventional methods in different noise conditions and low signal to noise ratios (SNRs).

Rohan ambani

Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using recordings of the output of the two microphones of a behind-the-ear hearing aid in response to male and female speech at various azimuths in the presence of noise produced by wind from various azimuths with a velocity of 3 m/s, using the “clean” speech as a reference. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective intelligibility and for sound quality/comfort. The conditions were: unprocessed noisy speech; noisy speech processed using the RNN; and noisy speech that was high-pass filtered (which also reduced wind noise). Eighteen native English-speaking participants were tested, nine with normal hearing and nine with mild-to-moderate hearing impairment. Frequency-dependent linear amplification was provided for the latter. Processing using the RNN was significantly preferred over no processing by both subject groups for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. High-pass filtering was not significantly preferred over no processing. Although RNN was significantly preferred over HPF only for sound quality for the hearing-impaired participants, for the results as a whole there was a preference for RNN over HPF. Overall, the results suggest that reduction of wind noise using an RNN is possible and might have beneficial effects when used in hearing aids.

Rajesh verma

Many hearing aid users are negatively impacted by wind noise when spending time outdoors. Turbulent airflow around hearing aid microphones caused by the obstruction of wind can result in noise that is not only perceived as annoying but may also mask desirable sounds in the listening environment, such as speech. To mitigate the adverse effects of wind noise, hearing aid developers have introduced several technological solutions to reduce the amount of wind noise at the hearing aid output. Some solutions are based on mechanical modifications; more recently, sophisticated signal processing algorithms have also been introduced. By offering solutions to the wind noise problem, these signal processing algorithms can promote more optimal use of hearing aids during outdoor activities. This article reviews how wind noise is generated in hearing aids, outlines the technological challenges in wind noise management, and summarizes the technological solutions that have been proposed and/or implemented in modern hearing aids.

kapil Roy

The sound quality of a turn signal sound is directly related to the driving safety and influences comfort during driving. A turn signal sound and interior noise always exist simultaneously while driving. However, none of the studies are performed to observe and control the change of a turn signal sound when the vehicle interior noise was changed. Therefore, this paper develops a method to evaluate and control the warning effect of a turn signal sound masked by vehicle interior noise. First, the turn signal sound and the vehicle interior noise at different speeds are measured and the time-frequency characteristics of the turn signal sound is analyzed. Second, the pairwise comparison method is applied to subjectively evaluate the warning effect of the sounds and the warning effect of the turn signal sound at 6 speeds relative to that at 40 km/h was obtained. Third, a new hearing threshold evaluating the warning effect is developed and used as a sound metric. This metric is employed to evaluate objectively the warning effect of warning sounds at different speeds. The subjective evaluation results are consistent with the objective evaluation results, which confirms the validity of objective evaluation. To keep the warning effect of the warning sound during the driving, two methods of controlling the turn signal sound are proposed and will be researched further in the in the future.

Prashant singh

This work presents a two-microphone speech enhancement (SE) framework based on basic recurrent neural network (RNN) cell. The proposed method operates in real-time, improving the speech quality and intelligibility in noisy environments. The RNN model trained using a simple feature set—real and imaginary parts of the short-time Fourier transform (STFT) are computationally efficient with a minimal input-output processing delay. The proposed algorithm can be used in any stand-alone platform such as a smartphone using its two inbuilt microphones. The detailed operation of the real-time implementation on the smartphone is presented. The developed application works as an assistive tool for hearing aid devices (HADs). Speech quality and intelligibility test results are used to compare the proposed algorithm to existing conventional and neural network-based SE methods. Subjective and objective scores show the superior performance of the developed method over several conventional methods in different noise conditions and low signal to noise ratios (SNRs).

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