Noisy Situations Research Articles

Self-reported hearing quality of life measures in pediatric cochlear implant recipients with bilateral input

Objective: Self-reported hearing quality of life (QoL) for pediatric cochlear implant (CI) recipients was examined, asking whether 1) children with CIs have similar QoL as those with less severe hearing loss (HL); 2) children with different bilateral CI (BCI) device configurations report different QoL; and 3) do audiological, demographic and spoken language factors affect hearing QoL?Design: One hundred four children (ages 7–11 years) using bimodal devices or BCIs participated. The Hearing Environments and Reflection of Quality of Life (HEAR-QL) questionnaire, receptive language and speech perception tests were administered. HEAR-QL scores of CI recipients were compared to scores of age-mates with normal hearing and mild to profound HL.Results: HEAR-QL scores for CI participants were similar to those of children with less severe HL and did not differ with device configuration. Emotion identification and word recognition in noise correlated significantly with HEAR-QL scores.Discussion: CI recipients reported that HL hinders social participation. Better understanding of speech in noise and emotional content was associated with fewer hearing-related difficulties on the HEAR-QL.Conclusions: Noisy situations encountered in educational settings should be addressed for children with HL. The link between perception of emotion and hearing-related QoL for CI recipients should be further examined.

Convolutional Neural Network Audio Classifier for Alarm Sound Detection

Neural Networks (ANN) has evolved through many stages in the last three decades with many researchers contributing in this challenging field. With the power of math complex problems can also be solved by ANNs. ANNs like Convolutional Neural Network (CNN), Deep Neural network, Generative Adversarial Network (GAN), Long Short Term Memory (LSTM) network, Recurrent Neural Network (RNN), Ordinary Differential Network etc., are playing promising roles in many MNCs and IT industries for their predictions and accuracy. In this paper, Convolutional Neural Network is used for prediction of Beep sounds in high noise levels. Based on Supervised Learning, the research is developed the best CNN architecture for Beep sound recognition in noisy situations. The proposed method gives better results with an accuracy of 96%. The prototype is tested with few architectures for the training and test data out of which a two layer CNN classifier predictions were the best.

De-noising of Signal using FIR Filters for Hearing Aids

In Today’s era health related issues are increasing day by day and one of them is hearing loss. Hearing aids have done wonders and helped people in this situation. Hearing aid is a small device that makes the sound loud and clear for the person who is unable to listen, communicate and could not participate actively in day to day activities. A hearing aid can help people to listen in different environments such as loudspeakers in the background, a loud train horn and different noisy situations etc. But the speech signals are degraded and corrupted by different types of noises such as train noise, car engine noise, loud speaker and traffic noise at different SNR levels like -2db, 5db, 10db. These speech signals need to be improved by removing the unwanted disturbances or interference called noise so that enhanced signal can be obtained with good quality for voice communication in hearing aids. This research work proposes the noise reduction technique using FIR filters for hearing aids. The approach of threshold will be applied to remove empty bands or unwanted noise from the noisy signal. The threshold based technique de-noises the input signal efficiently. The proposed approach is implemented in MATLAB and results are analyzed in terms of SNR (signal to noise ratio) and MSE (mean square error).

Speech-ABR in contralateral noise: A potential tool to evaluate rostral part of the auditory efferent system

In parallel with the auditory afferent system, the auditory efferent system is active in all parts of the auditory pathways from auditory cortex to the cochlea. The auditory efferent system has two main segments: the rostral and the caudal parts. The rostral part, which starts from the cortical centers to thalamic nuclei and continues into collicular pathways in rostral brainstem, which sends its neural fibers to the main collicular nuclei especially inferior colliculus. The caudal part includes the olivocochlear bundle, which originates from the superior olivary complex and terminates on the cochlear hair cells. Most studies about the auditory efferent system have focused only on the caudal part using otoacoustic emission suppression test. Speech-evoked auditory brainstem response (S-ABR) as an electrophysiologic test that uses speech stimuli to simulate real-life auditory conditions, reflects the performance of rostral brainstem centers, so structurally seems to be an appropriate candidate to examine the rostral part of the auditory efferent system. Our hypothesis is that S-ABR in noisy condition, a typical condition for stimulating the auditory efferent system, has the potential to be used as an objective noninvasive electrophysiologic test for studying the rostral auditory efferent system in diagnosis and treatment/rehabilitation follow-ups. In addition, S-ABR can potentially reflect higher-order auditory functions and the effects of their dysfunctions on the lower brainstem. This characteristic makes S-ABR even more suitable tool for evaluation of the efferent system. To evaluate our hypothesis, Eighteen normal hearing subjects in the age range of 18–25 were tested in a pilot study for S-ABR in quiet mode and three signal to noise ratios of +10,0 and −10 for both ears. Then we checked the correlation between the results of S-ABR in different conditions and scores of auditory behavioral tests that auditory efferent system is involved in them: Consonant-Vowel perception in noise, dichotic Consonant-Vowel-Consonant and sound lateralization in noise. The results of our pilot study showed a significant correlation between S-ABR changes in noise with the scores of the behavioral tests in noisy or dichotic situations. Findings of the current study suggest that S-ABR with specific contralateral noise can be an appropriate option for evaluating the performance of rostral part of the auditory efferent system and may be suitable for top-down auditory training follow-ups, although the generalization of these results needs further studies in different groups with different auditory processing abnormalities or skills.

An Offline-Sampling SMPC Framework With Application to Autonomous Space Maneuvers

In this paper, a sampling-based stochastic model predictive control (SMPC) algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. One of the main drivers for the development of the proposed control strategy is the need for reliable and robust guidance and control strategies for automated rendezvous and proximity operations between spacecraft. To this end, the proposed control algorithm is validated on a floating spacecraft experimental testbed, proving that this solution is effectively implementable in real time. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered. The approach enables to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided. Moreover, the offline sampling approach in the control design phase shifts all the intensive computations to the offline phase, thus greatly reducing the online computational cost, which usually constitutes the main limitation for the adoption of SMPC schemes, especially for low-cost on-board hardware. Numerical simulations and experiments show that the approach provides probabilistic guarantees on the success of the mission, even in rather uncertain and noisy situations, while improving the spacecraft performance in terms of fuel consumption.

Efficacy and Effectiveness of Advanced Hearing Aid Directional and Noise Reduction Technologies for Older Adults With Mild to Moderate Hearing Loss.

The purpose of the present study was to investigate the laboratory efficacy and real-world effectiveness of advanced directional microphones (DM) and digital noise reduction (NR) algorithms (i.e., premium DM/NR features) relative to basic-level DM/NR features of contemporary hearing aids (HAs). The study also examined the effect of premium HAs relative to basic HAs and the effect of DM/NR features relative to no features. Fifty-four older adults with mild-to-moderate hearing loss completed a single-blinded crossover trial. Two HA models, one a less-expensive, basic-level device (basic HA) and the other a more-expensive, advanced-level device (premium HA), were used. The DM/NR features of the basic HAs (i.e., basic features) were adaptive DMs and gain-reduction NR with fewer channels. In contrast, the DM/NR features of the premium HAs (i.e., premium features) included adaptive DMs and gain-reduction NR with more channels, bilateral beamformers, speech-seeking DMs, pinna-simulation directivity, reverberation reduction, impulse NR, wind NR, and spatial NR. The trial consisted of four conditions, which were factorial combinations of HA model (premium versus basic) and DM/NR feature status (on versus off). To blind participants regarding the HA technology, no technology details were disclosed and minimal training on how to use the features was provided. In each condition, participants wore bilateral HAs for 5 weeks. Outcomes regarding speech understanding, listening effort, sound quality, localization, and HA satisfaction were measured using laboratory tests, retrospective self-reports (i.e., standardized questionnaires), and in-situ self-reports (i.e., self-reports completed in the real world in real time). A smartphone-based ecological momentary assessment system was used to collect in-situ self-reports. Laboratory efficacy data generally supported the benefit of premium DM/NR features relative to basic DM/NR, premium HAs relative to basic HAs, and DM/NR features relative to no DM/NR in improving speech understanding and localization performance. Laboratory data also indicated that DM/NR features could improve listening effort and sound quality compared with no features for both basic- and premium-level HAs. For real-world effectiveness, in-situ self-reports first indicated that noisy or very noisy situations did not occur very often in participants' daily lives (10.9% of the time). Although both retrospective and in-situ self-reports indicated that participants were more satisfied with HAs equipped with DM/NR features than without, there was no strong evidence to support the benefit of premium DM/NR features and premium HAs over basic DM/NR features and basic HAs, respectively. Although premium DM/NR features and premium HAs outperformed their basic-level counterparts in well-controlled laboratory test conditions, the benefits were not observed in the real world. In contrast, the effect of DM/NR features relative to no features was robust both in the laboratory and in the real world. Therefore, the present study suggests that although both premium and basic DM/NR technologies evaluated in the study have the potential to improve HA outcomes, older adults with mild-to-moderate hearing loss are unlikely to perceive the additional benefits provided by the premium DM/NR features in their daily lives. Limitations concerning the study's generalizability (e.g., participant's lifestyle) are discussed.

A novel algorithm for the identification of dirac impulses from filtered noisy measurements

In this paper we address the recovery of a finite stream of Dirac pulses from noisy lowpass-filtered samples in the discrete-time setting. While this problem has been successfully addressed for the noise-free case using the concept of signals with finite rate of innovation, such techniques are not efficient in the presence of noise. In the FRI framework, the determination of the location of Dirac pulses is based on the singular value decomposition of a matrix whose rank in the noise-free case equals the number of Dirac pulses and the signal can be related to the non zero singular values. However, in noisy situations this matrix becomes full rank and the singular value decomposition is subject to subspace swap, meaning some singular values associated with noise become larger than some values related to the signal. This phenomenon has been recognized as the reason for performance breakdown in the method. The goal of this paper is to propose a novel algorithm that limits the alteration of these singular values in the presence of noise, thus significantly improving the estimation of Dirac pulses.

Speech Recognition of Cochlear Implant Users Inside a Noisy Helicopter Environment

In this study, we tested whether the speech recognition, through radio communications, of cochlear implant (CI) users inside a noisy helicopter cockpit is adequate for safe flight. Method: Speech recognition tests (sentences, numbers and disyllables) through a very-high-frequency radio were administered to 12 CI users in a soundproof booth, inside a helicopter with the engine turned off and turned on. Results: In quiet environments, radio communications were impacted only for disyllable intelligibility, but in noisy situations, all tests were affected. Conclusions: CI subjects did not achieve the auditory levels recommended by the International Civil Aviation Organization.

Tracking of Noise Tolerance to Predict Hearing Aid Satisfaction in Loud Noisy Environments.

A method that tracked tolerable noise level (TNL) over time while maintaining subjective speech intelligibility was reported previously. Although this method was reliable and efficacious as a research tool, its clinical efficacy and predictive ability of real-life hearing aid satisfaction were not measured. The study evaluated an adaptive method to estimate TNL using slope and variance of tracked noise level as criteria in a clinical setting. The relationship between TNL and subjective hearing aid satisfaction in noisy environments was also investigated. A single-blinded, repeated-measures design. Seventeen experienced hearing aid wearers with bilateral mild-to-moderately-severe sensorineural hearing loss. Participants listened to 82-dB SPL continuous speech and tracked the background noise level that they could "put up with" while subjectively understanding >90% of the speech material. Two trials with each babble noise and continuous speech-shaped noise were measured in a single session. All four trials were completed aided using the participants' own hearing aids. The stimuli were presented in the sound field with speech from 0° and noise from the 180° azimuth. The instantaneous tolerable noise level was measured using a custom program and scored in two ways; the averaged TNL (aTNL) over the 2-min trial and the estimated TNL (eTNL) as soon as the listeners reached a stable noise estimate. Correlation between TNL and proportion of satisfied noisy environments was examined using the MarkeTrak questionnaire. All listeners completed the tracking of noise tolerance procedure within 2 min with good reliability. Sixty-five percent of the listeners yielded a stable noise estimate after 59.9 sec of actual test time. The eTNL for all trials was 78.6 dB SPL (standard deviation [SD] = 4.4 dB). The aTNL for all trials was 78.0 dB SPL (SD = 3.3 dB) after 120 sec. The aTNL was 79.2 dB SPL (SD = 5.4 dB) for babble noise and 77.0 dB SPL (SD = 5.9 dB) for speech-shaped noise. High within-session test-retest reliability was evident. The 95% confidence interval was 1.5 dB for babble noise and 2.8 dB for continuous speech-shaped noise. No significant correlation was measured between overall hearing aid satisfaction and the aTNL (ρ = 0.20 for both noises); however, a significant relationship between aTNL and proportion of satisfied noisy situations was evident (ρ = 0.48 for babble noise and ρ = 0.55 for speech-shaped noise). The eTNL scoring method yielded similar results as the aTNL method although requiring only half the time for 65% of the listeners. This time efficiency, along with its reliability and the potential relationship between TNL and hearing aid satisfaction in noisy listening situations suggests that this procedure may be a good clinical tool to evaluate whether specific features on a hearing aid would improve noise tolerance and predict wearer satisfaction with the selected hearing aid in real-life loud noisy situations. A larger sample of hearing aid wearers is needed to further validate these potential uses.

Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data

Simultaneous-source acquisition, breaking the limit of conventional seismic acquisition, is a rapidly evolving research field, due to its advantage in reducing survey time and improving data quality. The benefits of simultaneous-source acquisition are compromised by the intense blending interference. Separating a blended record into a group of individual records, known as “deblending” is one of the most popular solution to the problem. However, the blended records are often corrupted by random noise, which causes difficulties in separation. In an iterative deblending algorithm, the incoherent interference can be simulated and subtracted from the blended record. When the random noise is strong, it is difficult to simulate the incoherent interference. In this paper, we propose a hybrid-sparsity constraint model that applies the dictionary learning into the deblending framework that is based on the sparsity-promoting transform to deal with extremely noisy simultaneous source data. The dictionary learning with fine-tuned adaptation can learn the incoherent interference into atoms and reject random noise. Then, the sparse transform-based framework is implemented to iteratively separate the signal and interference. We use two synthetic examples to demonstrate the advantage of the proposed method in extremely noisy situations. Two field examples further confirm the superior deblending performance of the proposed method for the noisy simultaneous-source data over the curvelet transform-based and rank reduction-based methods.

Fitting Intravoxel Incoherent Motion Model to Diffusion MR Signals of the Human Breast Tissue using Particle Swarm Optimization

Intravoxel incoherent motion (IVIM) modeling offers the parameters f, D and D* as biomarkers for different lesion types and cancer stages from diffusion MR signals. Challenges with the available optimization algorithms in fitting the model to the signals motive new studies for improved parameter estimations. In this study, one thousand value sets of f, D, D* for human breast are assembled and used to generate five thousand diffusion MR signals considering noise-free and noisy situations exhibiting signal-to-noise ratios (SNR) of 20, 40, 60 and 80. The estimates of f, D, D* are obtained using Levenberg-Marquardt (LM), trust-region (TR) and particle swarm (PS) algorithms. On average, the algorithms provide the highest fitting performance for the noise-free signals (R2adj=1.000) and great fitting performances on the noisy signals with SNR>20 (R2adj>0.988). TR algorithm performs slightly better for SNR=20 (R2adj=0.947). TR and PS algorithms achieve the highest parameter estimation performance for all the parameters while LM algorithm reveals the highest performance for f and D only on the noise-free signals (r=1.00). For the noisy signals, performances increase while SNR increases. All algorithms accomplish poor performances for D* (r=0.01-0.20) while TR and PS algorithms perform same for f (r=0.48-0.97) and D (r=0.85-0.99) but remarkably better than LM algorithm for f (r=0.08-0.97) and D (r=0.53-0.99). Overall, TR and PS algorithms demonstrate better but indistinguishable performances. Without requiring any user-given initial value, PS algorithm may facilitate improved estimation of IVIM parameters of the human breast tissue. Further studies are needed to determine its benefit in clinical practice.

Variation in speech perception in noise as a function of age in typically developing children

Introduction: Speech recognition performance has been observed to vary with the age of the listener. Children having difficulty in understanding speech in noisy situations is found to have an adverse effect on learning. The primary aim of the study was to determine the difference in word and phoneme scores in the presence of noise in three age groups of children studying in primary school. The secondary aim was to check whether there was a gender difference within an age group. Materials and Methods: Three groups of typically developing children (≥7 to

Efficient Pose Tracking based on Line Segment Matching

Pose tracking is a crucial issue for many applications such as robotic tasks and facility operations. Vision-based approaches with non-contact properties are appropriate choices for these tasks. However, vision-based approaches are not sufficiently robust and fast. In this work, we propose a vision-based pose tracking to deal with these problems. We estimate poses using Lie group and Lie algebra representation theory. Such operation is performed in a linearized space, therefore it is convenient for pose estimation. To provide reliable visual information for our pose estimation, we detect line segments. Our detection of line segment depends on semi-global image information. We describe all line segments and match those detected in consecutive frames. Our line segment detector and matching descriptor are good at discarding ambiguous line segments and finding real ones in noisy situations. The integration of group theory and line segment detection and matching plays an important role for developing a robust vision-based pose tracking system. Our system proves to be efficient and robust.

A new efficient two-channel fast transversal adaptive filtering algorithm for blind speech enhancement and acoustic noise reduction

This paper addresses the problem of speech enhancement and acoustic noise reduction by blind structures. Recently, the backward blind source separation (BBSS) structure has shown efficiency in cancelling the acoustic noise and improving corrupted speech signals form very noisy observations without any a priori information of source signals. In this paper, we propose a new algorithm based on the combination between the BBSS structure and the simplified fast transversal filter (SFTF) algorithm. The proposed two-channel simplified fast transversal filter (TCSFTF) algorithm succeeded an important blind improvement of steady state and convergence speed performances in diverse noisy situations when only the noisy signals are known. The performances of the new TCSFTF algorithm are compared with four state-of-the-art algorithms in different noisy conditions. This comparison is evaluated in terms of cepstral distance (CD), system mismatch (SM), segmental signal to noise ratio (SegSNR), and segmental mean square error (SegMSE) criteria.

Investigation of power quality disturbances by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms

Abstract The aim of this paper is to investigate the power quality analysis by using 2D discrete orthonormal S-transform, machine learning and multi-objective evolutionary algorithms. The fact that PQ signals are one-dimensional (1D) signals due to their nature leads to the search for feature extraction approaches based on 1D signal processing methods. Due to the electric network is getting more and more complicated day by day, it is necessary to determine effectively the disturbances events. In the proposed method, extraction of a new feature based on two-dimensional (2D) signal processing by 2D Fast Discrete Orthonormal Stockwell Transform (2D-FDOST) method and determination of the most suitable feature group by Non-dominated Sorting Genetic Algorithm II (NSGA-II) method are performed. Eleven different PQ events are synthetically produced based on mathematical modelling. 1D signals are transformed into 2D signals with equal row and column numbers. Statistical and image-based features are created on the amplitude and phase matrices obtained by 2D-FDOST method from 2D signals. The NSGA-II method, which is one of the multi-objective evolutionary optimization methods, is used to convert a large number of feature vectors into a small number of useful feature groups. NSGA-II produces the optimal solution for two different fitness functions that calculate the number of features and classifier performance. By using different machine learning classifiers for selected features, a model classifying PQ disturbances with high performance and robust structure against noisy situations is created.

Comparison of the projection onto convex sets and iterative hard thresholding methods for seismic data interpolation and denoising

Because of the environment limitations, irregularity appears in the observed seismic data. In addition, the observed seismic data contains random noise from the acquisition equipment and surrounding environment, which affects the performances of multi-channel techniques, such as surface related multiple elimination (SRME) and amplitude variation with offset (AVO) analysis. The projection onto convex sets (POCS) method, known as an efficient interpolation method, is suitable for high signal-to-noise ratio (SNR) situations; however, the existing random noise may affect its final performance. In our previously published paper, the POCS formula was deduced in the view of iterative hard thresholding (IHT) method using a projection operator. In this paper, more physical illustrations about its detailed deduction are provided to show the differences between IHT and POCS in noise-free and noisy situations with easy understanding for readers. Then, performances of the POCS and IHT methods are compared in both noise-free and noisy situations, in terms of seismograms, frequency wavenumber (FK) spectra and single traces. For noise-free data, both the POCS and IHT methods can achieve good interpolation results. For noisy data, the POCS method is unsuitable because of the observed noisy data insertion, while the IHT performance is satisfactory because it uses a thresholding operator to eliminate random noise. Numerical examples on noise-free datasets demonstrate the validities of the POCS and IHT methods for interpolation. Tests on noisy data contaminated with additive white Gaussian noise prove the ability and superiority of the IHT method with anti-noise property compared with the POCS method.

Adapting the speech, spatial and qualities of hearing scale into Malay – a preliminary study

ABSTRACTSelf-report inventories are important measures for assessing hearing abilities of individuals with hearing impairment. Thus the current study aimed to adapt the Speech, Spatial and Qualities of hearing scale (SSQ) into Malay. The SSQ scale in English was translated to Malay language, using the forward and backward approach. The Malay SSQ was administered on 50 adult hearing aid users. Cronbach's alpha was used to assess the reliability of the SSQ adapted into Malay language. One-way ANOVA was carried out to compare the scores between the domains measured in the Malay SSQ. The results showed the Malay SSQ has high internal consistency (Cronbach’s alpha = 0.97) and acceptable corrected item – correlations (0.86–0.35). The mean scores for the speech and spatial domain were significantly lower than the quality domain, suggesting lower satisfaction reported for the two domains. Telephone conversation and understanding speech in noisy situations were among items reported as most difficult listening task by the subjects. Understanding speech in a quiet room is rated as the easiest listening task. Age, degree of hearing loss and hearing aid experience did not correlate with the hearing disabilities. The internal consistency reliability of the adapted SSQ showed some degree of support to the appropriateness of the scale as an outcome measure. Further research is required to assess the validity and also the test-retest reliability of the Malay SSQ.

Two-channel forward NLMS algorithm combined with simple variable step-sizes for speech quality enhancement

This paper addresses the problem of speech quality enhancement by adaptive two-channel filtering algorithms. Recently, the forward blind source separation structure has been proposed and combined with normalized least-mean-square algorithm (FNLMS). The main drawback of two-channel FNLMS algorithm is its poor performance in steady state regime when the fixed step-sizes values are selected large. However, the slow convergence rate is observed with the small fixed step-size values. In this paper, we propose three new combinations of the basic FNLMS algorithm with simple variable step-sizes approaches, for improving both the steady state values and convergence rate (noted TVSF for Two-channel Variable Step-size Forward). In these modifications, we propose new configuration of two-channel forward structure by three simple and efficient variable step-sizes estimations. To confirm the good performance of three proposed TVSF algorithms compared with the classical fixed-step-size version, we have carried out several simulations in very noisy situations using several criteria.

Bias-Compensated Informed Sound Source Localization Using Relative Transfer Functions

In this paper, we consider the problem of estimating the target sound direction of arrival (DoA) for a hearing aid (HA) system, which can connect to a wireless microphone worn by the talker of interest. The wireless microphone “informs” the HA system about the noise-free target speech. To estimate the DoA, we consider a maximum-likelihood approach, and we assume that a database of DoA-dependent relative transfer functions (RTFs) has been measured in advance and is available. The proposed DoA estimator is able to take the available noise-free target speech, ambient noise characteristics, and the shadowing effect of the user's head on the received signals into account, and it supports both monaural and binaural microphone array configurations. Moreover, we analytically analyze the bias in the proposed estimator and introduce a modified estimator, which has been compensated for the bias. We demonstrate that the proposed method has lower computational complexity and better performance than recent RTF-based estimators. Furthermore, to decrease the number of parameters required to be wirelessly exchanged between the HAs in binaural configurations, we propose an information fusion strategy, which avoids transmitting microphone signals between the HAs. An important benefit of the proposed IF strategy is that the number of parameters to be exchanged between the HAs is independent of the number of HA microphones. Finally, we investigate the performance of variants of the proposed estimator extensively in different noisy and reverberant situations.

Effects of Including Information about Hidden Hearing Loss in an Adopt-A-Band Program on College Band Members' Attitudes toward Healthy Hearing Behaviors.

Young musicians may be at risk for developing cochlear synaptopathy (CS), or hidden hearing loss (HHL), that could lead to permanent music-induced hearing loss (MIHL). Patients with CS often complain of tinnitus and/or difficulty understanding speech in noisy situations, even though traditional audiometric testing indicates normal hearing. The aim of this article was to determine the effects of including information about HHL on an Adopt-A-Band program involving college band members' concern about and self-efficacy toward the prevention of MIHL. We conducted a single-blinded, randomized clinical trial. Forty-eight band members participated in this study. Band members were randomly assigned to two Adopt-A-Band presentations, one with and one without information on HHL. Including information about HHL had no effect on these band members' concerns about and self-efficacy toward the prevention of MIHL. However, the Adopt-A-Band program resulted in significantly increased concern for MIHL by 39.5% ( p < 0.0001, 95% confidence interval [CI]: 25-54.2), self-efficacy in its prevention by 79.1% ( p < 0.0001, 95% CI: 66.9-91.2), and plans to use musicians' earplugs while playing by 67.4% ( p < 0.0001, 95% CI: 53.4-81.45). Although inclusion of information about HHL did not have a significant effect, the Adopt-A-Band program, in general, significantly increased the immediate intent of these students to practice healthy hearing behaviors. Future research is needed to determine the long-term effects of using the Adopt-A-Band program with university marching bands' use of healthy hearing behaviors.