Trial Summary
What is the purpose of this trial?In this study, a noise reduction algorithm will be implemented in various listening situations to evaluate its effectiveness in improving speech understanding for cochlear implant users ages 12 and older.
Is the treatment SEDA promising for hearing loss?Yes, SEDA is promising because it helps people with hearing loss understand speech better in noisy environments by enhancing the clarity of speech sounds.12789
What safety data exists for SEDA treatment for hearing loss?The provided research does not directly address safety data for SEDA or related treatments. The studies focus on the effectiveness of various speech enhancement techniques for improving speech intelligibility in noise for hearing-impaired listeners. While these studies demonstrate improvements in speech perception, they do not provide specific safety data or evaluations of potential risks associated with the treatments.14567
What data supports the idea that SEDA for Hearing Loss is an effective treatment?The available research shows that SEDA for Hearing Loss can improve speech understanding for people with hearing impairments. In studies where speech was mixed with background noise, SEDA helped listeners with moderate to severe hearing loss understand speech better. Although the improvements were small, they were significant, meaning they were noticeable and important. This suggests that SEDA can be a helpful treatment for improving speech clarity in noisy environments.13579
Do I have to stop taking my current medications for the trial?The trial protocol does not specify whether you need to stop taking your current medications.
Eligibility Criteria
This trial is for cochlear implant users who are at least 12 years old and have been using their implant for over six months. Participants must be post-lingually deafened, meaning they lost hearing after acquiring language skills, and considered able to complete the study by the principal investigator.Inclusion Criteria
I am 12 years old or older.
Exclusion Criteria
I do not use a cochlear implant and am 12 years old or older.
Treatment Details
The trial is testing a new noise reduction algorithm called SEDA in various listening environments. The goal is to see if it helps people with cochlear implants understand speech better when there's background noise.
1Treatment groups
Experimental Treatment
Group I: Cochlear Implant UsersExperimental Treatment1 Intervention
Participants will complete up to 4 experiments evaluating the use of the Speech Enhancement using Dynamic thresholding Approach (SEDA) algorithm. Each testing session will take between 4-6 hours to complete. Testing sessions will continue until the completion of the experiments.
Find a clinic near you
Research locations nearbySelect from list below to view details:
Tisch HospitalNew York, NY
Loading ...
Who is running the clinical trial?
NYU Langone HealthLead Sponsor
National Institute on Deafness and Other Communication Disorders (NIDCD)Collaborator
References
Spectral enhancement to improve the intelligibility of speech in noise for hearing-impaired listeners. [2008]At speech-to-noise ratios between -3 and 6 dB, many hearing-impaired listeners have difficulty in understanding speech, but spectrograms reveal that the formant peaks of voiced speech and some of the spectral peaks associated with unvoiced speech stand out against the background noise. Our speech-enhancement process is based on the assumption that increasing spectral contrast will result in improved intelligibility. The enhancement involves calculating an auditory excitation pattern from the magnitude spectrum of overlapping short segments of the speech signal. This pattern is convolved with a difference-of-Gaussians function whose bandwidth varies with frequency in the same way as the auditory filter bandwidth. Magnitude values from this enhanced pattern are combined with the unchanged phase spectrum from the original signal to produce the enhanced speech. The processing was used to enhance Boothroyd and Bench-Kowal-Bamford Audiometric lists which had been digitally combined with speech-shaped noise at speech-to-noise ratios between -3 and 6 dB. The subjects had moderate to severe sensorineural hearing losses. The processing produced small but significant improvements in intelligibility for the hearing-impaired listeners tested. Possibilities for improving the processing are discussed.
Hearing aids using binaural processing principles. [2008]The performance of normal human hearing in noisy circumstances is helped considerably by its ability to focus on a specific sound source. This ability is derived from the binaural nature of hearing. In this paper we present signal-processing techniques that can partially replace the binaural hearing in situations where a monaural hearing aid is used (monaural deafness, cochlear implants). First, simple noise cancellation and beam forming solutions, which are applicable to speech, are presented. Then full adaptive filter versions and a successful combination of both are proposed. A key element to the success of all these algorithms is the use of a noise versus speech discrimination criterion on which basis selected parts of the adaptive filter structure are adapted at any given moment.
A comparison of two-channel and single-channel compression hearing aids. [2015]Eight subjects with bilateral sensorineural hearing losses took part in a trial comparing listening unaided with listening binaurally through two types of hearing aid, aid A and aid B. Both aids incorporated slow-acting automatic gain control (AGC) operating on the whole speech signal. However, aid A also incorporated two-channel syllabic compression. The two aids were chosen to be as similar as possible in other respects, and both were worn behind the ear. Subjects were tested in a counter-balanced order, and had at least 2 weeks of everyday experience with each aid before testing took place. Performance was evaluated in three ways: by measuring speech intelligibility in quiet for sentences at three peak sound levels, 55, 70 and 85 dB SPL; by measuring the level of speech required for 50% intelligibility (called the SRT) of sentences in two levels of speech-shaped noise, 60 and 75 dB SPL; and by administering questionnaires about experience with the aids in everyday life. Both aid A and aid B improved the intelligibility of speech in quiet relative to unaided listening, particularly at the lowest sound level. However, aid A gave lower (i.e., superior) SRTs in speech-shaped noise than aid B or unaided listening. The questionnaires also indicated that aid A gave better performance in noisy situations. The results strongly suggest that two-channel syllabic compression, combined with slow-acting AGC operating on the whole speech signal, can give superior results to slow-acting AGC alone, particularly in noisy situations.
Derivation of frequency-gain characteristics for maximizing speech reception in noise. [2019]Hearing aid gain-assignment schemes known as "prescriptions" were not designed for fitting hearing aids that modify their frequency responses to reduce background noise interference. Rather, prescriptions were developed for hearing aids having single, fixed frequency responses and aim to optimize speech reception in relatively quiet environments. Even though prescriptions do not apply to noisy conditions specifically, they embody the trade between maximizing speech audibility and maintaining loudness comfort that is critical to frequency-gain characteristic selection independent of whether noise is present or absent. The articulation index (AI) was used to examine the extent to which prescriptions' deference to loudness comfort causes them to fall short of maximizing speech spectrum audibility, thereby revealing (roughly) the magnitude of the loudness control built into prescriptions. AIs for speech amplified by an AI-maximizing rule (MAX AI) (Rankovic, Freyman, & Zurek, 1992) and according to several prescriptions were calculated as a function of hearing loss degree and configuration for quiet and noisy conditions. In quiet, AIs for prescriptions were similar to one another when presented with the same audiogram but were drastically smaller than MAX AIs, implying that prescriptions limit speech audibility to a large extent to prevent loudness discomfort. In noise, maximizing the AI required frequency-gain characteristics that were substantially different from prescription-assigned characteristics and that were unique to each noise/audiogram combination. A loudness constraint for the MAX AI scheme was developed to account for the gain discrepancy between prescription AIs and MAX AIs observed in the quiet condition, based on the highest comfortable loudness (HCL) equations presented by Cox (1989) in combination with a loudness model (von Paulus & Zwicker, 1972). The MAX AI scheme with the new loudness control was extended to specify frequency-gain characteristics expected to be optimal for several conditions containing noise, and examples are presented.
A speech enhancement scheme incorporating spectral expansion evaluated with simulated loss of frequency selectivity. [2019]Hearing-impaired listeners often suffer from supra-threshold speech perception deficits. One such deficit is reduced frequency selectivity. We applied a speech enhancement scheme that incorporated spectral expansion in an attempt to reduce the effects of this deficit. The speech processing could contain up to three stages, a first in which the peak-valley ratio of the speech spectrum was enlarged to counteract the broadening of the auditory filtering, and a second in which the overall speech spectrum was modified to counteract the effects of upward-spread-of-masking, using a linear filter. The third stage was a noise suppression stage, applied before the spectral enhancement. The effectiveness of the speech processing with and without noise suppression was evaluated for various parameter settings by measuring the speech reception threshold (SRT) in noise, i.e., the signal-to-noise ratio at which listeners repeat 50% of presented sentences correctly. We used normal-hearing subjects. To simulate the loss of frequency selectivity we applied spectral smearing to the stimuli presented to the subjects. The speech material of the SRT tests was mixed with the noise before processing, and, when present, the smearing was applied last. The results indicated that for one specific parameter setting the SRT values decreased (i.e., improved) by approximately 1 dB when incorporating the spectral expansion together with the linear filtering. Employing either of these two stages separately did not improve the SRT. The application of the noise suppression stage did not further improve the SRT. A pilot study using hearing-impaired listeners showed more promising results for a female than for a male speaker.
Speech Processing to Improve the Perception of Speech in Background Noise for Children With Auditory Processing Disorder and Typically Developing Peers. [2019]Auditory processing disorder (APD) may be diagnosed when a child has listening difficulties but has normal audiometric thresholds. For adults with normal hearing and with mild-to-moderate hearing impairment, an algorithm called spectral shaping with dynamic range compression (SSDRC) has been shown to increase the intelligibility of speech when background noise is added after the processing. Here, we assessed the effect of such processing using 8 children with APD and 10 age-matched control children. The loudness of the processed and unprocessed sentences was matched using a loudness model. The task was to repeat back sentences produced by a female speaker when presented with either speech-shaped noise (SSN) or a male competing speaker (CS) at two signal-to-background ratios (SBRs). Speech identification was significantly better with SSDRC processing than without, for both groups. The benefit of SSDRC processing was greater for the SSN than for the CS background. For the SSN, scores were similar for the two groups at both SBRs. For the CS, the APD group performed significantly more poorly than the control group. The overall improvement produced by SSDRC processing could be useful for enhancing communication in a classroom where the teacher's voice is broadcast using a wireless system.
Spectral Enhancement to Improve the Intelligibility of Speech in Noise for Hearing-impaired Listeners. [2020]At speech-to-noise ratios between -3 and 6 dB, many hearing-impaired listeners have difficulty in understanding speech, but spectrograms reveal that the formant peaks of voiced speech and some of the spectral peaks associated with unvoiced speech stand out against the background noise. Our speech-enhancement process is based on the assumption that increasing spectral contrast will result in improved intelligibility. The enhancement involves calculating an auditory excitation pattern from the magnitude spectrum of overlapping short segments of the speech signal. This pattern is convolved with a difference-of-Gaussians function whose bandwidth varies with frequency in the same way as the auditory filter bandwidth. Magnitude values from this enhanced pattern are combined with the unchanged phase spectrum from the original signal to produce the enhanced speech. The processing was used to enhance Boothroyd and Bench-Kowal-Bamford Audiometric lists which had been digitally combined with speech-shaped noise at speech-to-noise ratios between -3 and 6 dB. The subjects had moderate to severe sensorineural hearing losses. The processing produced small but significant improvements in intelligibility for the hearing-impaired listeners tested. Possibilities for improving the processing are discussed.
Hearing Aids Using Binaural Processing Principles. [2020]The performance of normal human hearing in noisy circumstances is helped considerably by its ability to focus on a specific sound source. This ability is derived from the binaural nature of hearing. In this paper we present signal-processing techniques that can partially replace the binaural hearing in situations where a monaural hearing aid is used (monaural deafness, cochlear implants). First, simple noise cancellation and beam forming solutions, which are applicable to speech, are presented. Then full adaptive filter versions and a successful combination of both are proposed. A key element to the success of all these algorithms is the use of a noise versus speech discrimination criterion on which basis selected parts of the adaptive filter structure are adapted at any given moment.
Optical Microphone-Based Speech Reconstruction System With Deep Learning for Individuals With Hearing Loss. [2023]Although many speech enhancement (SE) algorithms have been proposed to promote speech perception in hearing-impaired patients, the conventional SE approaches that perform well under quiet and/or stationary noises fail under nonstationary noises and/or when the speaker is at a considerable distance. Therefore, the objective of this study is to overcome the limitations of the conventional speech enhancement approaches.