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Development and real-time implementation of a wavelet based speech processing strategy with noise suppression for cochlear implants.

机译：基于小波的语音处理策略的开发和实时实现，并带有噪声抑制功能，用于人工耳蜗。

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摘要

Cochlear implants are prosthetic devices used to provide partial hearing in profoundly deaf people. The performance of cochlear implants deteriorates in noisy environments compared to quiet conditions. This reduction in performance has been attributed to various factors which include lower spectral and temporal resolution and inadequate noise suppression. In the first part of this dissertation, a wavelet-based speech processing strategy for cochlear implants is introduced. Three wavelet packet decomposition tree structures are considered and their performance in terms of computational complexity, spectral leakage, and fixed-point accuracy are compared to other commonly used strategies in cochlear implants. A recursive method of updating the wavelet coefficients is introduced to provide real-time processing. It is shown that the proposed strategy achieves higher analysis rates and a lower amount of spectral leakage than the existing strategies. In the second part of this dissertation, an adaptive cochlear implant system which is capable of classifying the background noise environment in real-time for the purpose of adjusting or tuning the noise suppression algorithm to that environment is introduced. The tuning is done automatically with no user intervention. Objective quality measures are used to show the superiority of this adaptive system compared to a conventional fixed noise-suppression system. Steps taken to achieve the real-time implementation of the entire system, incorporating both the cochlear implant speech processing and the background noise suppression, on the FDA approved PDA research platform for cochlear implant studies are reported along with the timing results.
机译：耳蜗植入物是用于为重度聋哑人提供部分听力的假体设备。与安静的环境相比，在嘈杂的环境中，人工耳蜗的性能会下降。性能的下降归因于各种因素，包括较低的频谱和时间分辨率以及不充分的噪声抑制。在本文的第一部分，介绍了一种基于小波的人工耳蜗语音处理策略。考虑了三种小波包分解树结构，并将它们在计算复杂性，频谱泄漏和定点精度方面的性能与人工耳蜗中其他常用策略进行了比较。介绍了一种更新小波系数的递归方法以提供实时处理。结果表明，所提出的策略比现有策略具有更高的分析率和更低的频谱泄漏量。在本论文的第二部分，介绍了一种自适应耳蜗植入系统，该系统能够对背景噪声环境进行实时分类，以针对该环境调整或调整噪声抑制算法。调整是自动完成的，无需用户干预。与传统的固定噪声抑制系统相比，使用客观的质量度量来显示该自适应系统的优越性。报告了在FDA批准的用于耳蜗植入物研究的PDA研究平台上，结合了耳蜗植入物语音处理和背景噪声抑制的，为实现整个系统的实时实施而采取的步骤以及计时结果。

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  • 作者

    Gopalakrishna, Vanishree Bijadi.;

  • 作者单位

    The University of Texas at Dallas.;

  • 授予单位 The University of Texas at Dallas.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 127 p.
  • 总页数 127
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 康复医学;
  • 关键词

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