Seminars in Hearing最新文献

Children with hearing loss require early access to sound in both ears to support their development. In this article, we describe barriers to providing bilateral hearing and developmental consequences of delays during early sensitive periods. Barriers include late identification of hearing loss in one or both ears and delayed access to intervention with hearing devices such as cochlear implants. Effects of delayed bilateral input on the auditory pathways and brain are discussed as well as behavioral effects on speech perception and other developmental outcomes including language and academics. Evidence for these effects has supported an evolution in cochlear implant candidacy in children that was started with unilateral implantation in children with profound deafness bilaterally to bilateral implantation to implantation of children with asymmetric hearing loss including children with single-side deafness. Opportunities to enhance the developmental benefits of bilateral hearing in children with hearing loss are also discussed including efforts to improve binaural/spatial hearing and consideration of concurrent vestibular deficits which are common in children with hearing loss.

Cochlear implantation (CI) is the preferred method of hearing rehabilitation when patients cannot perform well with traditional amplification. Unfortunately, there are still significant misconceptions around this life-changing intervention. The goal of this article is to address some of the most common myths around CI surgery. After reading this article, the learner will be able to explain the utility of CI in patients with residual hearing and recognize that insurance coverage is widespread. The reader will be able to list common risks associated with this well-tolerated procedure including anesthetic risk and the risk of vestibular dysfunction. Additionally, the reader will be able to identify the significant positive impact of CI on patients' quality of life. Finally, the reader will identify that many patients can safely have an MRI scan after implantation, including nearly all contemporary recipients.

Potential cochlear implant (CI) candidates arrive to the clinic with a variety of hearing loss configurations, hearing aid history, and aided capabilities. CI candidacy is primarily determined based on aided speech recognition capability, which relies on benefit derived from use of hearing aids. Therefore, contemporary evaluation for CI candidacy should incorporate a battery of testing to determine abilities and limitations and must be predicated on appropriate verification of the hearing aid fitting. However, recent reports, including a retrospective chart review of patients presenting to Cleveland Clinic for CI evaluation, suggest that a significant subset of patients may be using inappropriately fit or programmed amplification. Thus, a combination of simulated real-ear measurements and aided speech recognition testing is essential for fully assessing the effect of amplification and ultimately determination of CI candidacy. Furthermore, waiting to incorporate these tools until CI candidacy is suspected may delay timely identification of problems or need to change technology. Utilization of evidence-based decision drivers ultimately leads clinicians to timely patient-specific interventions which may include surgical intervention or other amplification options. As audiology moves into a healthcare era in which payers consider the benefit of our services to overall health and well-being, demonstrating timely, optimal outcomes using thorough, multifactorial evaluation is essential.

Hearing aids continue to acquire increasingly sophisticated sound-processing features beyond basic amplification. On the one hand, these have the potential to add user benefit and allow for personalization. On the other hand, if such features are to benefit according to their potential, they require clinicians to be acquainted with both the underlying technologies and the specific fitting handles made available by the individual hearing aid manufacturers. Ensuring benefit from hearing aids in typical daily listening environments requires that the hearing aids handle sounds that interfere with communication, generically referred to as "noise." With this aim, considerable efforts from both academia and industry have led to increasingly advanced algorithms that handle noise, typically using the principles of directional processing and postfiltering. This article provides an overview of the techniques used for noise reduction in modern hearing aids. First, classical techniques are covered as they are used in modern hearing aids. The discussion then shifts to how deep learning, a subfield of artificial intelligence, provides a radically different way of solving the noise problem. Finally, the results of several experiments are used to showcase the benefits of recent algorithmic advances in terms of signal-to-noise ratio, speech intelligibility, selective attention, and listening effort.

A requirement for modern hearing aids is to evaluate a listening environment for the user and automatically apply appropriate gain and feature settings for optimal hearing in that listening environment. This has been predominantly achieved by the hearing aids' acoustic sensors, which measure acoustic characteristics such as the amplitude and modulation of the incoming sound sources. However, acoustic information alone is not always sufficient for providing a clear indication of the soundscape and user's listening needs. User activity such as being stationary or being in motion can drastically change these listening needs. Recently, hearing aids have begun utilizing integrated motion sensors to provide further information to the hearing aid's decision-making process when determining the listening environment. Specifically, accelerometer technology has proven to be an appropriate solution for motion sensor integration in hearing aids. Recent investigations have shown benefits with integrated motion sensors for both laboratory and real-world ecological momentary assessment measurements. The combination of acoustic and motion sensors provides the hearing aids with data to better optimize the hearing aid features in anticipation of the hearing aid user's listening needs.

For many years, clinicians have understood the advantages of listening with two ears compared with one. In addition to improved speech intelligibility in quiet, noisy, and reverberant environments, binaural versus monaural listening improves perceived sound quality and decreases the effort listeners must expend to understand a target voice of interest or to monitor a multitude of potential target voices. For most individuals with bilateral hearing impairment, the body of evidence collected across decades of research has also found that the provision of two compared with one hearing aid yields significant benefit for the user. This article briefly summarizes the major advantages of binaural compared with monaural hearing, followed by a detailed description of the related technological advances in modern hearing aids. Aspects related to the communication and exchange of data between the left and right hearing aids are discussed together with typical algorithmic approaches implemented in modern hearing aids.

There are two parts to this article. The first is a general overview of how hearing aid classification works, including a comparison study of normal-hearing listeners and multiple manufacturers' hearing aids while listening to a sound parkour composed of a multitude of acoustic scenes. Most hearing aids applied nearly identical classification for simple listening environments. But differences began to appear across manufacturers' products when the listening environments became more complex. The second section reviews the results of a study of the acoustic ecology (listening environments) experienced by several cohorts of hearing aid users over a 4-month period. The percentages of time people spent in seven different listening environments were mapped. It was learned that they spent an average of 57% of their time in conversation and that age is not a good predictor of the amount of time spent in most listening environments. This is because, when grouped by age, there was little to no difference in the distribution of time spent in the seven listening environments, whereas there was tremendous variability within each age group.

This article details ways that machine learning and artificial intelligence technologies are being integrated in modern hearing aids to improve speech understanding in background noise and provide a gateway to overall health and wellness. Discussion focuses on how Starkey incorporates automatic and user-driven optimization of speech intelligibility with onboard hearing aid signal processing and machine learning algorithms, smartphone-based deep neural network processing, and wireless hearing aid accessories. The article will conclude with a review of health and wellness tracking capabilities that are enabled by embedded sensors and artificial intelligence.

Understanding speech in noise is difficult for individuals with normal hearing and is even more so for individuals with hearing loss. Difficulty understanding speech in noise is one of the primary reasons people seek hearing assistance. Despite amplification, many hearing aid users still struggle to understand speech in noise. In response to this persistent problem, hearing aid manufacturers have invested significantly in developing new solutions. Any solution is not without its tradeoffs, and decisions must be made when optimizing and implementing them. Much of this happens behind the scenes, and casual observers fail to appreciate the nuances of developing new hearing aid technologies. The difficulty of communicating this information to clinicians may hinder the use or the fine-tuning of the various technologies available today. The purpose of this issue of Seminars in Hearing is to educate professionals and students in audiology, hearing science, and engineering about different approaches to combat problems related to environmental and wind noise using technologies that include classification, directional microphones, binaural signal processing, beamformers, motion sensors, and machine learning. To accomplish this purpose, some of the top researchers and engineers from the world's largest hearing aid manufacturers agreed to share their unique insights.