HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. An Optimum Inexact Design for an Energy Efficient Hearing Aid Sai Praveen Kadiyala, Aritra Sen, Shubham Mahajan, Quingyun Wang, Avinash Lingamaneni, James Sneed German, Hong Xu, Krishna Palem, Arindam Basu

/pdf/an-optimum-inexact-design-for-an-energy-efficient-hearing-2dswlquhqv.pdf

An optimum inexact design for an energy efficient hearing aid

Hearing aids are a field of medical devices, powered by digital signal processing techniques. Development of hearing aid modules is realized usually in the first stages using prototyping boards equipped with specialized Embedded DSP. The paper presents a generic model of a hearing aid developed on a DSP development system. The method used to develop and prototype the model (Rapid prototyping) is the core contribution of the paper. Starting from a standard algorithm flow, the system allows to demonstrate and to experiment the basic principles and processing algorithms in hearing aids. The purpose is mainly educational, but with the potential for a commercial application. The main advantage is the possibility to visualize, analyze and enhance every step of the processing flow. To develop, test and demonstrate the functionality we used simple Virtual Instrumentation concepts, making possible to keep all the components of the test bench in a single PC.

A Rapid Prototyping model concept for a DSP based hearing aid

Accurate speech quality measures are highly attractive and beneficial in the design, finetuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications.

/pdf/learning-based-reference-free-speech-quality-assessment-for-5csqk2whyh.pdf

Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

In this paper, we describe the basic principle of the wide dynamic range compression algorithm, the hardware structure of digital hearing aids, and the structure of core and peripheral devices of DSP5535. On this basis, through the introduction of the realization of the wide dynamic range compression algorithm, this paper describes how to optimize the algorithm in hearing aid platform based on the DSP5535. In the optimization method, some optimization methods make the required cycles of algorithm related operations to reduce 2–3 times, which fully demonstrates the effectiveness of the optimization method. These optimization methods play an important role for the implement of WDRC algorithm in the real-time processing of speech signals.

Optimized realization of wide dynamic range compression based on DSP5535 hearing aid platform

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Optimized realization of wide dynamic range compression based on DSP5535 hearing aid platform