Dataset Information

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications.

ABSTRACT: Alert signals like sirens and home alarms are important as they warn people of precarious situations. This work presents the detection and separation of these acoustically important alert signals, not to be attenuated as noise, to assist the hearing impaired listeners. The proposed method is based on convolutional neural network (CNN) and convolutional-recurrent neural network (CRNN). The developed method consists of two blocks, the detector block, and the separator block. The entire setup is integrated with speech enhancement (SE) algorithms, and before the compression stage, used in a hearing aid device (HAD) signal processing pipeline. The detector recognizes the presence of alert signal in various noisy environments. The separator block separates the alert signal from the mixture of noisy signals before passing it through SE to ensure minimal or no attenuation of the alert signal. It is implemented on a smartphone as an application that seamlessly works with HADs in real-time. This smartphone assistive setup allows the hearing aid users to know the presence of the alert sounds even when these are out of sight. The algorithm is computationally efficient with a low processing delay. The key contribution of this paper includes the development and integration of alert signal separator block with SE and the realization of the entire setup on a smartphone in real-time. The proposed method is compared with several state-of-the-art techniques through objective measures in various noisy conditions. The experimental analysis demonstrates the effectiveness and practical usefulness of the developed setup in real-world noisy scenarios.

SUBMITTER: Bhat GS

PROVIDER: S-EPMC7423022 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications.

Bhat Gautam Shreedhar GS Shankar Nikhil N Panahi Issa M S IMS

IEEE access : practical innovations, open solutions 20200101

Alert signals like sirens and home alarms are important as they warn people of precarious situations. This work presents the detection and separation of these acoustically important alert signals, not to be attenuated as noise, to assist the hearing impaired listeners. The proposed method is based on convolutional neural network (CNN) and convolutional-recurrent neural network (CRNN). The developed method consists of two blocks, the detector block, and the separator block. The entire setup is in ...[more]

PMID: 32793404

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Project description:ObjectivesPrevious work has suggested that individual characteristics, including amount of hearing loss, age, and working memory ability, may affect response to hearing aid signal processing. The present study aims to extend work using metrics to quantify cumulative signal modifications under simulated conditions to real hearing aids worn in everyday listening environments. Specifically, the goal was to determine whether individual factors such as working memory, age, and degree of hearing loss play a role in explaining how listeners respond to signal modifications caused by signal processing in real hearing aids, worn in the listener's everyday environment, over a period of time.DesignParticipants were older adults (age range 54-90 years) with symmetrical mild-to-moderate sensorineural hearing loss. We contrasted two distinct hearing aid fittings: one designated as mild signal processing and one as strong signal processing. Forty-nine older adults were enrolled in the study and 35 participants had valid outcome data for both hearing aid fittings. The difference between the two settings related to the wide dynamic range compression and frequency compression features. Order of fittings was randomly assigned for each participant. Each fitting was worn in the listener's everyday environments for approximately 5 weeks before outcome measurements. The trial was double blind, with neither the participant nor the tester aware of the specific fitting at the time of the outcome testing. Baseline measures included a full audiometric evaluation as well as working memory and spectral and temporal resolution. The outcome was aided speech recognition in noise.ResultsThe two hearing aid fittings resulted in different amounts of signal modification, with significantly less modification for the mild signal processing fitting. The effect of signal processing on speech intelligibility depended on an individual's age, working memory capacity, and degree of hearing loss. Speech recognition with the strong signal processing decreased with increasing age. Working memory interacted with signal processing, with individuals with lower working memory demonstrating low speech intelligibility in noise with both processing conditions, and individuals with higher working memory demonstrating better speech intelligibility in noise with the mild signal processing fitting. Amount of hearing loss interacted with signal processing, but the effects were small. Individual spectral and temporal resolution did not contribute significantly to the variance in the speech intelligibility score.ConclusionsWhen the consequences of a specific set of hearing aid signal processing characteristics were quantified in terms of overall signal modification, there was a relationship between participant characteristics and recognition of speech at different levels of signal modification. Because the hearing aid fittings used were constrained to specific fitting parameters that represent the extremes of the signal modification that might occur in clinical fittings, future work should focus on similar relationships with more diverse types of signal processing parameters.

Project description:ImportanceHearing loss is a highly prevalent condition with multiple negative associated outcomes, yet few persons with hearing loss have hearing aids (HAs). Personal sound amplification products (PSAPs) could be an alternative low-cost solution to HAs, but data are lacking on the performance of PSAPs.ObjectiveTo evaluate the clinical efficacy of a PSAP by comparing its performance with that of a basic HA and a premium HA in participants with mild, moderate, and moderately severe hearing impairment.Design, setting, and participantsA prospective, single-institution cohort study was performed with a total of 56 participants, including 19 with mild hearing loss, 20 with moderate hearing loss, and 17 with moderately severe hearing loss. All participants underwent 4 clinical hearing tests with each of the PSAP, basic HA, and premium HA, and all completed an evaluative questionnaire.InterventionsAll hearing devices (PSAP, basic HA, and premium HA) were applied by a clinician to prevent bias and order effects; participants were blinded to the device in use, and sequence of devices was randomized.Main outcomes and measuresThe study used the Korean version of the hearing in noise test, the speech intelligibility in noise test, listening effort measurement using a dual-task paradigm, pupillometry, and a self-rating questionnaire regarding sound quality and preference. These tests were administered under the following 4 hearing conditions: unaided hearing, use of PSAP, use of basic HA, and use of premium HA.ResultsThe study included 56 participants with a mean age of 56 years (interquartile range, 48-59 years); 29 (52%) were women. In the mild and moderate hearing loss groups, there was no meaningful difference between PSAP, basic HA, and premium HA for speech perception (Cohen d = 0.06-1.05), sound quality (Cohen d = 0.06-0.71), listening effort (Cohen d = 0.10-0.92), and user preference (PSAP, 41%; basic HA, 28%; premium HA, 31%). However, for the patients with moderately severe hearing loss, the premium HA had better performance across most tests (Cohen d = 0.60-1.59), and 70% of participants preferred to use the premium HA.Conclusions and relevanceThe results indicate that basic and premium HAs were not superior to the PSAP in patients with mild to moderate hearing impairment, which suggests that PSAPs might be used as an alternative to HAs in these patient populations. However, if hearing loss is more severe, then HAs, especially premium HAs, should be considered as an option to manage hearing loss.

Dataset Information

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications.

Publications

Design and Integration of Alert Signal Detector and Separator for Hearing Aid Applications.

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