Interpreting the efficacy of frequency-lowering algorithms

Kuk, Francis; Keenan, Denise; Auriemmo, Jane; Korhonen, Petri; Peeters, Heidi; Lau, Chi; Crose, Bryan

doi:10.1097/01.hj.0000370858.58483.2e

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Several studies have shown that sound therapy is an effective method to decrease the tinnitus-related distress, emotional reaction to and awareness of tinnitus (Kuk et al, 2010;Sweetow and Sabes, 2010;Krick et al, 2015;Liu et al, 2021). Sound therapies were initially based on broadband noise but now include noise shaped according to the audiogram (Henry et al, 2015), narrowband signals focusing on the frequency of tinnitus (Hoare et al, 2014b), notch noise (Jin et al, 2021), nature sounds (Barozzi et al, 2016;Sereda et al, 2017), music (Krick et al, 2015), or fractal tones (Tyler et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Impact of a combination sound therapy on tinnitus distress: an exploratory one-year longitudinal study

Lelic,

Caporali,

Parker

et al. 2024

Front. Audiol. Otol.

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IntroductionCombination devices offering both amplification and sound therapy are commonly used in tinnitus management. However, there is insufficient evidence supporting the relationship between sound therapy and tinnitus outcomes. The aim of this study was to explore longitudinal effects of sound therapy on tinnitus-related distress using a combination device.MethodTwenty participants with mild to moderate tinnitus related distress were fitted with combination devices that included three sound-therapy programs. The sound-therapy programs were selected by the participants from the available sounds offered in the combination device. The sounds comprised fractal music, nature sounds and combinations of the two. Participants were asked to wear the devices for 12 months and to complete questionnaires related to tinnitus distress at baseline and 1, 2, 4, 6, and 12 months after starting the treatment. Additionally, at 2, 4, 6, and 12 months, the device log data capturing information about amplification and sound-therapy use were collected.ResultsTinnitus handicap inventory (THI), tinnitus functional index (TFI), tinnitus awareness and annoyance decreased following the device fitting. This improvement plateaued at 4 months. The degrees of improvement in THI, TFI and tinnitus annoyance were correlated with daily hours of sound-therapy use but not with daily hours of amplification-only or total device use.ConclusionsA combination sound therapy consisting of therapy sounds, amplification as needed, and counseling was associated with a reduction in tinnitus-related distress. A future randomized controlled trial should be conducted to allow for detangling the effect of sound therapy from effects of amplification, counseling, placebo, and time itself, and to investigate the predictors of sound-therapy benefit and use.

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Section: Introductionmentioning

confidence: 99%

Impact of a combination sound therapy on tinnitus distress: an exploratory one-year longitudinal study

Lelic,

Caporali,

Parker

et al. 2024

Front. Audiol. Otol.

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Marathi Speech Intelligibility Enhancement Using I-AMS Based Neuro-Fuzzy Classifier Approach for Hearing Aid Users

et al. 2022

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Globally, 1.6 billion individuals suffered from hearing disability in 2019. According to the World Health Organization, by 2050, the number of people with hearing impairments will rise to 2.5 billion. Speech perception in noisy surroundings is a challenge for hearing aid users. This study aimed to design a novel methodology to improve the speech recognition ability of hearing aid users from various backgrounds. To improve speech enhancement, we propose a discrete cosine transform (DCT)-based improved amplitude-magnitude spectrogram (I-AMS) algorithm with a fuzzy classifier. First, the I-AMS approach disintegrates speech signals containing noise into time-frequency units and eliminates the noise present in the signal. Next, the time frequency units (t-f units), modulation frequency (fm), and centre frequency (fc) are extracted from the denoised signal. A neuro-fuzzy classifier was used to classify the background speech environment into three different classes. The proposed I-AMS algorithm was tested, achieved improvements in terms of sensitivity (+1.02%) and accuracy (+11.80%). Speech denoising revealed a 1.27% improvement in speech recognition performance.

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