CN109982224A - 自适应滤波算法在助听器中的运用 - Google Patents
自适应滤波算法在助听器中的运用 Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R1/00—Details of transducers, loudspeakers or microphones
- H04R1/20—Arrangements for obtaining desired frequency or directional characteristics
- H04R1/22—Arrangements for obtaining desired frequency or directional characteristics for obtaining desired frequency characteristic only
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
- H04R25/00—Deaf-aid sets, i.e. electro-acoustic or electro-mechanical hearing aids; Electric tinnitus maskers providing an auditory perception
- H04R25/50—Customised settings for obtaining desired overall acoustical characteristics
- H04R25/505—Customised settings for obtaining desired overall acoustical characteristics using digital signal processing
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Abstract
声反馈消除系统是基于自适应系统辨识的理论基础,本章主要研究自适应滤波算法用于系统辨识的原理;自适应滤波算法是现代信号处理的重要内容,它可以在系统和信号统计特性的先验知识未知的条件下,通过自学来获得,并跟踪环境的随机变化,自适应地调整滤波器参数并最终逼近理论上的最优滤波——维纳滤波,在诸多领域有着广泛的应用;自适应滤波器的结构有脉冲响应滤波器(FIR),无限脉冲响应滤波器(IIR),格型结构和非线性滤波器等,而FIR滤波器实现简单,容易设计成线性相位,是应用最广泛的结构。
Description
技术领域
本发明的自适应滤波器采用某种代价函数和准则,自适应调整滤波器系数以使代价函数满足准则的要求,最终逼近最佳估计的滤波器;自适应滤波器不需要任何关于信号和噪声的先验知识,在迭代过程中逐渐获得信号的统计特性,通过自学达到最优滤波,一定条件下,与维纳滤波等价,并且能够感知信号统计特性的变化,重新调整系数,使得在变化后的系统中仍是最优的。
背景技术
相比模拟助听器来讲,数字助听器听力补偿效果好,内部噪声小,语音失真小,能明显提高言语辨别能力;数字助听器的这些优点主要仰仗DSP处理器,DSP处理器的强大的处理能力才使得优秀的算法得以实现;但是仅仅有数字硬件平台还是远远不够的,软件实现的算法才是数字助听器的灵魂,算法的目的是要使听觉舒适,得到最高的言语理解率;数字助听器向微型化、智能化发展,因此算法不仅需要实现基本功能,提高算法的性能,又要尽量满足算法的实时实现,做到运算量小,耗能量少,所需存储空间少,在性能和实时实现中求得平衡。
发明内容
本发明的解决方案直接由误差决定,对噪声非常敏感,在高信噪比时,误差信号能很好的反应收敛程度并跟踪系统变化,因而有较快的收敛速度和很好的跟踪能力,但是低信噪比时,误差不能很好跟踪系统变化,将会严重影响算法的性能;在声反馈消除系统中,干扰噪声不是高斯信号,而是环境噪声和目标语音之和,语音信号幅度变化较大,这类算法若应用在数字助听器声反馈消除中,容易受到干扰的影响,使得误差不能准确的反映收敛程度,步长受目标语音信号的影响较大,性能大打折扣;需要较大的步长加快收敛速度,随着迭代次数增加,然后逐步减小步长,确保自适应过程临近稳态时步长较小,滤波器权值才能在最优值附近有较小的失调量。
具体实施方式
本发明实施如下,利用脉冲响应的结构的稀疏特点获得比NLMS算法更快的收敛速度,但是其收敛速度和稳态误差的矛盾关系仍然存在;稳态失调量和步长控制矩阵G(n)无关,而由全局步长参数μ调节;解决这个问题一个有效方法就是变步长;变步长的主要思想是找到某种能够反映滤波器系统变化的物量,然后建立这个物理量与步长的某种关系,使得自适应算法的步长随该物理量呈规律变化;自适应过程开始时,滤波器的权值远离维纳解,需要较大的步长加快收敛速度,随着迭代次数增加,然后逐步减小步长,确保自适应过程临近稳态时步长较小,滤波器权值才能在最优值附近有较小的失调量。
Claims (1)
1.本发明是自适应学习过程从系数空间的任意一点开始出发沿着性能曲面的负梯度方向搜索最低点,自动调整权系数,使权矢量对应的均方误差能达到最小值的过程;梯度向量在算法初始阶段时较大,滤波器权系数会以较大的速度向维纳解收敛,然后迭代次数增加,梯度逐渐减小,算法收敛后梯度接近零;梯度方向在算法迭代初始阶段是一致的,接近稳态的时候,算法在最低点附近抖动,梯度方向变化较频繁,但是绝对值较小;因此受观测噪声影响更小,并通过加窗计算平均梯度,平滑了噪声,减小瞬时值的影响,提高了衡量算法收敛程度的准确度,因此算法在噪声大的情况下也能表现好的性能。
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