A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies.Aim: To review technologies that will enable the next generations of digital therapies for tinnitus.Methods: A “state-of-the-art” review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011–2021. The search strategy used the following key words: “tinnitus” and [“HA,” “personalized therapy,” “AI” (and “methods” or “applications”), “Virtual reality,” “Games,” “Sensors” and “Transducers”], and “Hearables.” Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes.Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined.Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

Wavelet packets-based speech enhancement techniques for digital hearing aids

Hearing-impaired listeners often have great difficulty understanding speech in a noisy background. The problem has motivated the development of a new speech enhancement scheme with the goal of improving speech in noise perception for the hearing impaired listeners. In this thesis, a novel wavelet packet based noise reduction algorithm and hearing loss compensation are presented for a single microphone hearing aids application. The noise reduction scheme utilizes noise masking threshold based suppression rule to remove additive noise. The perceptual noise suppression rule is optimized to achieve a balance between noise removal and speech distortion. Both objective and subjective evaluations have shown superior performance of the proposed technique in a good combination of low residual noise and low signal distortion. The hearing loss compensation is realized by the wavelet-based loudness compression in each critical band. The compensated speech is guaranteed above hearing-impaired listener's threshold of hearing and with growth of loudness corrected in the dynamic range. Preference test among normal hearing person with simulated hearing loss has shown compensated speeches are favored in various conditions.

Wavelet packets-based speech enhancement techniques for digital hearing aids

Hearing-impaired listeners often have great difficulty understanding speech in a noisy background. The problem has motivated the development of a new speech enhancement scheme with the goal of improving speech in noise perception for the hearing impaired listeners. In this thesis, a novel wavelet packet based noise reduction algorithm and hearing loss compensation are presented for a single microphone hearing aids application. The noise reduction scheme utilizes noise masking threshold based suppression rule to remove additive noise. The perceptual noise suppression rule is optimized to achieve a balance between noise removal and speech distortion. Both objective and subjective evaluations have shown superior performance of the proposed technique in a good combination of low residual noise and low signal distortion. The hearing loss compensation is realized by the wavelet-based loudness compression in each critical band. The compensated speech is guaranteed above hearing-impaired listener's threshold of hearing and with growth of loudness corrected in the dynamic range. Preference test among normal hearing person with simulated hearing loss has shown compensated speeches are favored in various conditions.

A method for enhancing speech and warning signals based on parallel convolutional neural networks in a noisy environment

BACKGROUND: Digital hearing aids are based on technology that amplifies sound and removes noise according to the frequency of hearing loss in hearing loss patients. However, within the noise removed is a warning sound that alert the listener; the listener may be exposed to danger because the warning sound is not recognized. OBJECTIVE: In this paper, a deep learning model was used to improve these limits and propose a method to distinguish the warning sound in speech signals mixed with noise. In addition, the improved speech and warning sound were derived by removing noise present in the classification sound signals. METHODS: To classify the sound dataset, an adaptive convolution filter that changes according to two signals is proposed. The proposed convolution filter is applied to the PCNNs model to analyze the characteristics of the time and frequency domains of the dataset and classify the presence or absence of warning sound. In addition, the CEDN model was used to improve the intelligibility of the warning and the speech in the signal based on the warning sound classification from the proposed PCNNs model. RESULTS: Experimental results show that the PCNNs model using the proposed multiplicative filters is efficient for analyzing sound signals with complex frequencies. In addition, the CEDN model was used to improve the intelligibility of the warning and the speech in the signal based on the warning sound classification from the proposed PCNNs model. CONVLUSION: We confirmed that the PCNN model with the proposed filter showed the highest training rate, lowest error rate, and the most stable results. In addition, the CEDN model confirmed that speech and warning sounds were recognized, but it was confirmed that there was a limitation in clearly recognizing speech as the noise ratio increased.

A Survey on Application Specific Processor Architectures for Digital Hearing Aids

On the one hand, processors for hearing aids are highly specialized for audio processing, on the other hand they have to meet challenging hardware restrictions. This paper aims to provide an overview of the requirements, architectures, and implementations of these processors. Special attention is given to the increasingly common application-specific instruction-set processors (ASIPs). The main focus of this paper lies on hardware-related aspects such as the processor architecture, the interfaces, the application specific integrated circuit (ASIC) technology, and the operating conditions. The different hearing aid implementations are compared in terms of power consumption, silicon area, and computing performance for the algorithms used. Challenges for the design of future hearing aid processors are discussed based on current trends and developments.