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CN105103218B - 环境噪声均方根(rms)检测器 - Google Patents

环境噪声均方根(rms)检测器 Download PDF

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CN105103218B
CN105103218B CN201380072664.2A CN201380072664A CN105103218B CN 105103218 B CN105103218 B CN 105103218B CN 201380072664 A CN201380072664 A CN 201380072664A CN 105103218 B CN105103218 B CN 105103218B
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A·A·米拉尼
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Abstract

一种使用具有可变平滑因子的一阶回归量的RMS检测器经修改以惩罚来自数据中心的样本以便获得RMS值。与背景噪声电平相差极大的样本在RMS计算中受到抑制。当背景噪声改变时，系统将跟踪背景噪声的改变且在经校正RMS值的计算中包含所述改变。最小值跟踪器跟踪用以计算正规化距离值的最小rms值以使平滑因子正规化。经校正或经修正RMS值经确定为前一RMS值乘以1减所述平滑因子的差加所述平滑因子乘所述最小rms值的函数以输出针对本发明的所述经校正RMS。rms值用于为所述最小值跟踪器产生复位信号且举例来说，在背景信号随时间增加/降低时用于在所述跟踪器中避免死锁。

Description

环境噪声均方根(RMS)检测器
技术领域
本发明涉及一种环境噪声均方根(RMS)电平检测器。特定来说，本发明针对于一种经改进噪声RMS检测器，其对语音的存在、风噪声及噪声电平的其它突然变化为稳健的。
背景技术
例如无线电话的个人音频装置包含适应性噪声消除(ANC)电路，所述ANC电路从参考麦克风信号适应性地产生抗噪声信号且将所述抗噪声信号注入到扬声器或其它换能器输出中以产生环境音频声音的消除。还接近扬声器提供错误麦克风以测量环境声音及换能器附近的换能器输出，因此提供对噪声消除的有效性的指示。处理电路使用参考及/或错误麦克风(任选地)以及经提供以用于捕获近端语音的麦克风，以确定ANC电路是否正在不正确地适应于或可能不正确地适应于瞬时声环境及/或抗噪声信号是否可能为不正确的及/或破坏性的，且接着在处理电路中采取行动以防止或补救此类状况。
此类适应性噪声消除系统的实例揭示于在2012年6月7日公开的公开美国专利申请案2012/0140943中及在2012年8月16日公开的公开美国专利申请案2012/0207317中，所述两者以引用方式并入本文中。这两个参考与本申请案受让于同一受让人且共同命名至少一个发明者，且因此并不是本申请案的现有技术而是经提供以促进对如在使用领域中应用的ANC电路的不充分陈述。
现在参考图1，根据本发明的一实施例图解说明的无线电话10经展示为接近于人耳5。无线电话10包含换能器(例如扬声器SPKR)，所述换能器重现由无线电话10接收的遥远语音以及用以提供平衡对话感知的其它局部音频事件(例如铃音、所存储音频程序材料、近端语音(即，无线电话10的用户的语音)的注入)，以及需要由无线电话10重现的其它音频，例如来自网页或由无线电话10接收的其它网络通信的源以及例如电池量低及其它系统事件通知的音频指示。近语音麦克风NS经提供以捕获从无线电话10传输到其他对话参与者的近端语音。
无线电话10包含适应性噪声消除(ANC)电路及特征，所述ANC电路及特征将抗噪声信号注入到扬声器SPKR中以改进遥远语音及由扬声器SPKR重现的其它音频的可理解性。参考麦克风R经提供以用于测量周围声环境且经定位远离用户/谈话者的嘴的典型位置，使得近端语音在由参考麦克风R产生的信号中被最小化。第三麦克风(错误麦克风E)经提供以便在无线电话10紧密接近于耳朵5时，通过提供对环境音频与由接近于耳朵5的扬声器SPKR再现的音频的组合的测量而进一步改进ANC操作。无线电话10内的示范性电路14包含音频CODEC集成电路20，所述音频CODEC集成电路从参考麦克风R、近语音麦克风NS及错误麦克风E接收信号且与含有无线电话收发器的其它集成电路(例如RF集成电路12)介接。
一般来说，ANC技术测量撞击于参考麦克风R上的环境声事件(与扬声器SPKR的输出及/或近端语音相反)，且通过也测量撞击于错误麦克风E上的相同环境声事件，所图解说明无线电话10的ANC处理电路调适从参考麦克风R的输出产生的抗噪声信号以具有使错误麦克风E处的环境声事件的振幅最小化的特性。由于声路径P(z)(也称为被动正向路径)从参考麦克风R延伸到错误麦克风E，因此ANC电路本质上与移除电声路径S(z)(也称为次级路径)的效应相组合地估计声路径P(z)，所述电声路径S(z)表示CODEC IC 20的音频输出电路的响应及扬声器SPKR的包含特定声环境中扬声器SPKR与错误麦克风E之间的耦合的声/电传送功能，所述特定声环境在无线电话并非被稳固地压到耳朵5时受耳朵5及可接近于无线电话10的其它物理对象及人体头部结构的接近及结构影响。
此类适应性噪声消除(ANC)系统可采用均方根(rms)检测器来检测平均背景噪声电平。此RMS检测器需要缓慢地跟踪背景噪声电平但并不是如此缓慢以至于变得对环境变化不敏感。理想的RMS检测器应为对语音存在稳健的，对麦克风上的刮擦(接触)稳健的，对风噪声稳健的且具有低计算复杂性。出于描述本环境噪声RMS检测器的目的，小写rms变量用来指代现有技术的技术且大写RMS用来表示本环境噪声RMS检测器的经校正信号，如下文所陈述。本环境噪声RMS检测器可在产生RMS信号时利用现有技术rms值。
最著名的背景噪声估计方法(基于最小统计)可能是由雷尼尔马丁(RanierMartin)引入的rms检测器。参见以引用方式并入本文中的马丁，雷尼尔的基于最优平滑及最小统计的噪声功率频谱密度估计(Noise Power Spectral Density Estimation Basedon Optimal Smoothing and Minimum Statistics)(IEEE语音及音频处理会报，第9栏，第5号，2001年7月)以及同样以引用方式并入本文中的马丁，雷尼尔的基于最小统计的频谱减法(Spectral Subtraction Based on Minimum Statistics)(在1994年9月13日到16日于英国爱丁堡的第7届EUSIPCO'94会刊中第1182页到1195页)。伊斯雷尔科恩(Israel Cohen)已基于马丁的设计制作了另一RMS检测器。参见以引用方式并入本文中的科恩，伊斯雷尔的不利环境中的噪声频谱估计：经改进的最小值控制的递归平均(Noise SpectrumEstimation in Adverse Environments:Improved Minima Controlled RecursiveAveraging)(IEEE语音及音频处理会报，第11卷，第5期，2003年9月)以及同样以引用方式并入本文中的科恩，伊斯雷尔的用于稳健的语音增强的由最小值控制的递归平均进行的噪声估计(Noise Estimation by Minima Controlled Recursive Averaging for RobustSpeech Enhancement)(IEEE信号处理快报，第9卷，第1号，2002年1月)。马丁及科恩两者的方法及设计采用跟踪最小RMS值的方法。两种方法还使用具有可变平滑因子的一阶回归量。
与马丁的设计相比，科恩的设计可较不复杂且提供较好性能。科恩的设计取决于应针对不同应用而进行调整的几个阈值及参数。由于保留先前的rms值来找出最小值，因此科恩的设计还比马丁的设计使用更少的存储器。科恩的设计的问题是其易受非平稳噪声(例如尖峰噪声)的影响。举例来说，当用于蜂窝式电话或类似物上的适应性噪声消除系统(ANC)中时，例如风噪声或刮擦(用户/谈话者的手刮擦或摩擦外壳)等尖峰噪声可形成尖峰，科恩的设计将对所述尖峰过度反应。因此，(举例来说)蜂窝式电话或类似物中的ANC系统的性能可由于rms检测器对这些尖峰噪声过度反应而降级。
基于一阶回归的简单的rms检测器可产生图2中所图解说明的输出。此一阶回归可如方程式(1)中所展示而计算：
其中α表示平滑因子，rms(n)表示样本n的rms值且input(n)表示样本n的输入信号，且n为样本整数号。因此，通过将平滑因子(从1减去)乘以前一rms值且接着加上输入值的绝对值乘以此同一平滑因子而计算方程式(1)中的rms值。平滑因子α可取决于输入信号的绝对值是大于还是小于前一rms值而从两个值αatt或αdec中的一者选择。
此简单rms检测器的问题是其不仅跟踪背景噪声，而且还跟踪语音、刮擦及风噪声。如图2中所图解说明，外较暗线210表示语音信号，其具有如所展示的偶然的尖峰噪声220。较亮线230表示以缓慢进攻及快速衰减计算的rms信号，如方程式(1)中所展示。如在图2中可见，使用方程式(1)计算的rms值230以最终跟踪这些尖峰信号220，此对于适应性噪声消除(ANC)电路可能为不合意的。通过跟踪尖峰信号220，ANC电路可最终产生不适当的抗噪声且因此在用于用户的重现音频信号中形成伪声。
发明内容
本环境噪声RMS检测器表示从适应性或机器学习角度对现有技术rms检测器的改进。本环境噪声RMS检测器使用k-NN(使用最近相邻者进行分类)算法的概念以便获得RMS值。k-最近相邻者算法(k-NN)是用于基于特征空间中的最靠近训练例子将对象进行分类的方法。k-NN为一类基于实例的学习或即时学习，其中函数仅经局部近似且所有计算经推迟直至分类为止。将对象按其相邻者的多数表决而分类，其中将对象指派为其k个最近相邻者(k为通常小的正整数)当中最常见的类。如果k＝1，那么将对象简单地指派为其最近相邻者的类。
通过简单地将对象的性质值指派为其k个最近相邻者的值的平均值，相同方法可用于回归。其对加权相邻者的贡献可为有用的，使得较近相邻者比较远相邻者对平均值的贡献更多。(常见加权方案为赋予每一相邻者1/d的权重，其中d为到相邻者的距离。此方案为线性内插的概述。)
本发明并入有使用具有可变平滑因子的一阶回归量的现有技术rms检测器，但添加了额外特征以惩罚来自数据中心的样本以便获得RMS值。因此，与背景噪声电平(例如语音、刮擦及其它噪声尖峰)相差极大的样本在RMS计算中受到抑制。然而，当背景噪声增加/降低(一般来说，改变)时，系统将跟踪背景噪声的此改变且在经校正RMS值的计算中包含所述改变。
将来自使用具有可变平滑因子的一阶回归量的现有技术rms检测器的输出馈送到最小值跟踪器，此也为此项技术中已知的。所述最小值跟踪器随时间跟踪最小rms值Rmin。使用此经修正的最小值来计算正规化距离值d，所述正规化距离值表示表达为先前所计算rms值与本环境噪声RMS检测器中所计算的RMS值之间的差的绝对值除以由本环境噪声RMS检测器所计算的RMS值的比率。此值d继而用于通过将平滑因子α除以d或1的最大值来使所述平滑因子正规化。
一旦计算了这些值，便可将经校正或经修正RMS值确定为前一RMS值乘以1减平滑因子的差加平滑因子乘最小rms值的函数，以输出本环境噪声RMS检测器的经校正RMS。rms值可用于为最小值跟踪器产生复位信号。此复位信号可以大约0.1秒到1秒操作且举例来说，在背景信号随时间增加时用于在跟踪器中避免死锁。
本环境噪声RMS检测器(如本文所附的图中所展示)的效应为提供尤其在与现有技术的技术相比时其值在很大程度上不受例如由于语音、“刮擦”(举例来说，当人身体上触摸麦克风时)或风噪声所致的突然尖峰的影响的背景RMS值。
虽然本文中在蜂窝式电话及其中所使用的适应性噪声消除电路的上下文中进行讨论，但本环境噪声RMS检测器具有用于若干个音频装置及类似物的应用。举例来说，本发明的RMS检测器可应用于音频及音频-视觉记录装备、配备有麦克风的计算装置、语音辨识系统、语音激活系统(例如，在汽车中)及甚至事件检测器(其中从突然噪声(例如玻璃破碎或闯入者的语音)过滤背景声音可为合意的)，例如报警系统。虽然在蜂窝式电话及适应性噪声消除电路的上下文中进行揭示，但本环境噪声RMS检测器决不应解释为限于所述特定应用。
附图说明
图1是图解说明双麦克风可如何用于蜂窝式电话中的适应性噪声消除电路中的图式。
图2是图解说明具有尖峰分量的话音信号及使用现有技术的技术的所得rms信号计算的图表。
图3是本环境噪声RMS检测器的实施例的框图。
图4是图解说明如何跟踪最小RMS值的图表。
图5A是图解说明针对包括背景噪声与语音的样本输入信号的瞬时RMS及环境RMS的图表。
图5B是图解说明根据方程式(7)及图3中的方框160而从瞬时RMS计算的值α的图表。
图5C是图解说明根据方程式(6)及图3的方框150进行的距离值d的计算的图表。
图5D是图解说明如依据下文方程式(2)及图3的方框140所确定的所得Rmin的值的图表。
图6是将含有背景噪声的信号与语音进行比较的图表，其展示现有技术的旧方法与本环境噪声RMS检测器的技术及设备之间的比较。
图7是将含有背景噪声的信号与背景噪声中的“刮擦”信号进行比较的图表，其展示现有技术的旧方法与本环境噪声RMS检测器的技术及设备之间的比较。
具体实施方式
本环境噪声RMS检测器通过在RMS检测器中使用经改进算法而改进例如由马丁及科恩所教示的现有技术rms检测器的技术。图3是本环境噪声RMS检测器的框图。参考图3，使用已知的现有技术的技术从输入信号计算原始rms值。方框110、120及130为具有可变平滑因子的一阶回归量的要素。输入信号(其在此实例中可为背景噪声信号与语音)经馈送到其中采取所述信号的绝对值的方框110。此绝对值信号继而经馈送到低通滤波器120且接着经馈送到降低取样频率取样器130。净效应为输出例如上文结合方程式(1)所描述的原始rms值。由于所述框图的这前三个要素为此项技术中已知的，因此将不会进一步详细地描述所述要素。
上文所讨论的马丁及科恩两者的方法及设计还采用跟踪最小rms值Rmin的方法，且跟踪最小rms值为本环境噪声RMS检测器的一个功能。语音、麦克风上的刮擦(身体接触)、风噪声及任何尖峰噪声不太可能都是背景噪声，这是因为其等并非始终存在而是显现为环境噪声信号中的噪声尖峰。可通过将短期最小RMS值与长期最小RMS值进行比较来利用此事实以确定是否已发生此尖峰。图4是图解说明如何跟踪最小RMS值的图表。针对每一瞬时转变，短期rms值Rmin及Rtmp可经计算为：
其中Rmin为随时间的最小rms值，且Rtmp为用以跟踪背景噪声改变的暂时最小rms值。
接着用方程式(2)同时计算环境噪声检测器的复位机制。此复位机制每0.1秒到1秒将值Rmin及Rtmp的长期rms值计算为：
如图4中所图解说明，此方法具有响应于背景噪声rms值BK rms的基础rms计算的改变而延迟最小RMS值Rmin的改变的效应。当背景rms信号从电平A增加到电平B时，根据上文方程式(2)及(3)所计算的暂时最小值Rtmp从电平A上升到电平B，随时间延迟，如图4中所图解说明。最小RMS值Rmin的值从电平A上升到电平B，甚至进一步地延迟(从电平B降低到电平A同样如此)，如图4中所图解说明。尽管图4仅展示其中电平A小于电平B的情形，但相同效应也在电平A大于电平B时发生。
在科恩的依据此最小RMS值Rmin计算的方法中，基于背景噪声信号中扰动的存在概率而使用第一方法计算RMS可为可能的：
此处，p(l)为任何扰动(例如，语音存在)的存在概率，且当此概率接近1时，平滑因子值接近1。此概率值可如下来计算：
其中αp表示平滑因子，且δ为确定任何扰动与Rmin(l)相比的电平的阈值。
此RMS跟踪技术的一个问题是存在太多参数需要调整。另外，其反应时间为缓慢的且并不稳健。语音rms可泄漏到背景RMS值。虽然现有技术的科恩设计具有额外组件以使系统更稳健，但所述系统仍遭受这些相同的操作问题。因此，本环境噪声RMS检测器改进方程式(4)及(5)的算法以提供经改进的最小RMS值Rmin跟踪技术及RMS计算。
返回参考图3，在本环境噪声RMS检测器中，所输出的原始rms值接着经馈送到最小值跟踪器140。在方框150中，目前RMS与瞬时rms值之间的正规化距离d经计算为：
其中rms(l)为样本l的原始rms值且RMS(l)为经校正RMS因子。
在方框160中，平滑因子使用此距离d而正规化：
其中αd(l)表示样本l的正规化平滑因子且α0表示标准平滑因子，且max(d,l)为正规化距离与1的最大值。所述正规化平滑因子接着经馈送到方框170：
RMS(l)＝(1-αd(l))·RMS(l-1)+αd(l)·Rmin(l)| (8)
其中RMS(l)为经校正RMS值，且RMS(l-1)为前一经校正RMS值，αd(l)表示如在方程式(7)中所计算的样本l的正规化平滑因子且最小RMS值Rmin为在方程式(3)中所计算的最小rms值。
原始rms值还经馈送到方框190，其接着产生复位信号Reset。复位信号Reset经触发以便使系统复位以(举例来说)在背景噪声信号逐渐上升时避免任何死锁。复位机制经展示于如先前所讨论的方程式(3)中。
图4到6是图解说明本环境噪声RMS检测器的操作的图表。在图5A中，展示针对包括背景噪声与语音的样本输入信号的瞬时RMS及环境RMS。在图5A中，背景噪声显现为基线信号510且语音部分在中央显现为升高部分520。瞬时rms显现为粗线(510、520)，而最终所计算的环境RMS显现为粗线下方的细线530。在图5B中，展示了根据上文方程式(7)及图3中的方框160而从瞬时rms计算的值α。图5C展示根据上文方程式(6)及图3的方框150进行的d的计算。图5D展示如依据上文方程式(8)及图3的方框170所确定的所得最小RMS值Rmin。
图6是将含有背景噪声的信号与语音进行比较的图表，其展示现有技术的旧方法与本发明的技术及设备之间的比较。在图6中rms(l)信号经展示为在中央部分中具有语音扰动620的宽的暗信号610。使用现有技术方法的rms计算经展示为所述信号的中央中的波状亮线630。如图6中所展示，在此信号中出现相对于源信号的尖峰。如图6中所图解说明，现有技术的技术对背景噪声信号中的语音敏感。底部线640表示使用本环境噪声RMS检测器的技术所计算的RMS值。如图6中所图解说明，本环境噪声RMS检测器的技术对瞬态尖峰的响应性远不及现有技术的技术。
图7是将含有背景噪声710的信号与背景噪声中的刮擦信号720进行比较，且展示现有技术的旧方法与本环境噪声RMS检测器的技术及设备之间的比较的图表。刮擦信号720比图6的语音信号620更显著。rms(l)信号在图7中经展示为宽的暗信号710。使用现有技术方法的rms计算经展示为所述信号的中央中的波状亮线730。如图7中所展示，在此信号中出现相对于源信号710的尖峰720。底部线740表示使用用于本环境噪声RMS检测器的技术所计算的RMS值。如图7中所图解说明，本环境噪声RMS检测器的技术对瞬态尖峰的响应性远不及现有技术的技术。
因此，已证明本环境噪声RMS检测器从输入信号更准确地计算RMS值，同时相对来说不受语音、风噪声、刮擦及其它信号尖峰的影响。此经改进的RMS值计算为供用于(举例来说)蜂窝式电话或类似物中的适应性噪声消除(ANC)电路提供更好的输入值。此经改进值继而允许对ANC电路的更好操作，在输出到用户的音频中形成更少的伪声或被丢掉的音频(例如，由于ANC电路过度补偿所期望的音频信号及使所期望的音频信号静音而导致)。
虽然本文中已详细揭示并描述本环境噪声RMS检测器的实施例，但所属领域的技术人员可明了，可在不背离本发明的精神及范围的情况下在实施例中做出形式及细节的各种改变。

Claims (32)

1.一种均方根RMS检测器，其检测背景噪声输入信号的RMS电平同时实质上不受话音、风、刮擦声音及任何尖峰噪声的影响，所述RMS检测器包括：
原始rms检测器，其接收背景噪声输入信号且输出原始rms值；
最小rms跟踪器，其接收所述原始rms值且跟踪所述原始rms值的最小rms值；
正规化距离跟踪器，其接收所述原始rms值且计算所述原始rms值与经校正RMS值之间的距离值；
正规化平滑因子计算器，其通过将平滑因子除以所述距离值或1的最大值而使所述平滑因子正规化；以及
RMS值计算器，其依据所述最小rms值、前一经校正RMS值及所述正规化平滑因子而确定经校正RMS值，且输出经校正RMS值。
2.根据权利要求1所述的RMS检测器，其进一步包括
复位产生器，其接收所述原始rms值，且产生到所述最小rms跟踪器的复位信号以在所述原始rms值的值随时间改变时使所述最小rms跟踪器复位以防止所述最小rms跟踪器锁定。
3.根据权利要求2所述的RMS检测器，其中所述原始rms检测器通过将前一原始rms值与输入信号值相加而确定原始rms。
4.根据权利要求3所述的RMS检测器，其中在与所述前一原始rms值相加之前，将所述输入信号值的绝对值乘以平滑因子。
5.根据权利要求4所述的RMS检测器，其中在与所述输入信号值相加之前，将所述前一原始rms值乘以1减所述平滑因子的差。
6.根据权利要求5所述的RMS检测器，其中所述平滑因子取决于所述输入信号的所述绝对值是大于还是小于所述前一原始rms值而选自两个预定值中的一者。
7.根据权利要求2所述的RMS检测器，其中所述原始rms检测器通过以下方程式确定原始rms：
其中α表示平滑因子，rms(n)表示样本n的所述原始rms值且input(n)表示样本n的所述输入信号，且其中n为样本号，且平滑因子α可取决于所述输入信号的绝对值是大于还是小于前一原始rms值而选自两个值αatt或αdec中的一者。
8.根据权利要求2所述的RMS检测器，其中所述最小rms值跟踪器通过采取前一最小rms值与当前原始rms值的最小值而确定短期最小rms值，且
每0.1秒到1秒，将长期最小rms值计算为前一暂时最小rms值与目前原始rms值的最小值以使所述检测器复位，其中所述前一暂时最小rms值跟踪背景噪声改变。
9.根据权利要求8所述的RMS检测器，其中所述最小rms值跟踪器每0.1秒到1秒将所述前一暂时最小rms值设定为当前原始rms值且将所述最小rms值设定为前一暂时rms值与所述当前原始rms值的最小值以更密切地跟踪所述最小rms值。
10.根据权利要求9所述的RMS检测器，其中通过将所述原始rms值与所述经校正RMS值之间的差除以所述经校正RMS值而计算所述正规化距离。
11.根据权利要求10所述的RMS检测器，其中通过将标准预定平滑因子除以所述正规化距离与1的所述最大值而计算所述正规化平滑因子。
12.根据权利要求11所述的RMS检测器，其中由所述RMS检测器输出的所述经校正RMS值是通过所述正规化平滑因子乘由所述最小rms值跟踪器确定的所述最小rms值与所述前一经校正RMS值乘1减所述正规化平滑因子的差的乘积的和来计算。
13.根据权利要求2所述的RMS检测器，其中所述最小rms值跟踪器通过采取前一最小rms值与当前原始rms值的最小值而确定所述最小rms值
且每0.1秒到1秒，长期rms值Rmin及Rtmp可经计算为：
以使所述检测器复位，其中Rmin为随时间的所述最小rms值，且Rtmp为用以跟踪背景噪声改变的暂时最小rms值。
14.根据权利要求13所述的RMS检测器，其中通过以下方程式计算所述正规化距离d：
其中rms(l)为样本l的原始rms值且RMS(l)为经校正RMS值。
15.根据权利要求14所述的RMS检测器，其中通过以下方程式计算所述正规化平滑因子：
其中αd(l)表示样本l的所述正规化平滑因子且α0表示标准平滑因子，且max(d,1)为所述正规化距离与1的所述最大值。
16.根据权利要求15所述的RMS检测器，其中通过以下方程式计算由所述RMS检测器输出的所述经校正RMS值：
RMS(l)＝(1-αd(l))·RMS(l-1)+αd(l)·Rmin(l)
其中RMS(l)为所述经校正RMS值，且RMS(l-1)为前一经校正RMS值，αd(l)表示由正规化平滑因子计算器确定的样本l的所述正规化平滑因子，且Rmin为由所述最小rms值跟踪器所确定的所述最小rms值。
17.在RMS检测器中，一种检测背景噪声输入信号的RMS电平同时实质上不受话音、刮擦、风声音及任何尖峰噪声影响的方法，所述方法包括：
在接收背景噪声输入信号的初始RMS检测器中产生原始rms值；
在接收所述原始rms值的最小rms跟踪器中跟踪所述原始rms值的最小rms值；
在接收所述原始rms值的正规化距离跟踪器中计算所述原始rms值与经校正RMS值之间的距离值；
在正规化平滑因子计算器中，通过将平滑因子除以所述距离值或1的最大值而使所述平滑因子正规化；以及
在RMS值计算器中，通过依据所述最小rms值、前一经校正RMS值及所述正规化平滑因子确定经校正RMS值而计算经校正RMS值。
18.根据权利要求17所述的方法，其进一步包括：
在接收所述原始rms值的复位产生器中，产生到所述最小rms跟踪器的复位信号以在所述原始rms值的值随时间改变时使所述最小rms跟踪器复位以防止所述最小rms跟踪器锁定。
19.根据权利要求18所述的方法，其中原始rms检测器通过将前一原始rms值与输入信号值相加而确定原始rms。
20.根据权利要求19所述的方法，其中在与所述前一原始rms值相加之前，将所述输入信号值的绝对值乘以平滑因子。
21.根据权利要求20所述的方法，其中在与所述输入信号值相加之前，将所述前一原始rms值乘以1减所述平滑因子的差。
22.根据权利要求21所述的方法，其中取决于所述输入信号的所述绝对值是大于还是小于所述前一原始rms值而从两个预定值中的一者选择所述平滑因子。
23.根据权利要求18所述的方法，其中所述原始rms检测器通过以下方程式确定原始rms：
其中α表示平滑因子，rms(n)表示样本n的所述rms值且input(n)表示样本n的所述输入信号，且其中n为样本号，且平滑因子α可取决于所述输入信号的绝对值是大于还是小于前一原始rms值而选自两个值αatt或αdec中的一者。
24.根据权利要求18所述的方法，其中所述最小rms值跟踪器通过采取前一最小rms值与当前原始rms值的最小值而确定短期最小rms值，且
每0.1秒到1秒，将长期最小rms值计算为前一暂时最小rms值与目前原始rms值的最小值以使所述检测器复位，其中所述前一暂时最小rms值跟踪背景噪声改变。
25.根据权利要求24所述的方法，其中所述最小rms值跟踪器每0.1秒到1秒将所述前一暂时最小rms值设定为当前原始rms值且将所述最小rms值设定为前一暂时rms值与所述当前原始rms值的最小值以更密切地跟踪所述最小值。
26.根据权利要求25所述的方法，其中通过将所述原始rms值与所述经校正RMS值之间的差除以所述经校正RMS值而计算所述正规化距离。
27.根据权利要求26所述的方法，其中通过将标准预定平滑因子除以所述正规化距离与1的所述最大值而计算所述正规化平滑因子。
28.根据权利要求27所述的方法，其中由所述RMS检测器输出的所述经校正RMS值是通过所述正规化平滑因子乘由所述最小rms值跟踪器确定的所述最小rms值与所述前一经校正RMS值乘1减所述正规化平滑因子的差的乘积的和来计算。
29.根据权利要求18所述的方法，其中所述最小rms值跟踪器通过采取前一最小rms值与当前原始rms值的最小值而确定所述最小rms值
且每0.1秒到1秒，可将长期rms值Rmin及Rtmp计算为：
以使所述检测器复位，其中Rmin为随时间的所述最小rms值，且Rtmp为用以跟踪背景噪声改变的暂时最小rms值。
30.根据权利要求29所述的方法，其中通过以下方程式计算所述正规化距离d：
其中rms(l)为样本l的原始rms值且RMS(l)为经校正RMS值。
31.根据权利要求30所述的方法，其中通过以下方程式计算所述正规化平滑因子：
其中αd(l)表示样本l的所述正规化平滑因子且α0表示标准平滑因子，且max(d,1)为所述正规化距离与1的所述最大值。
32.根据权利要求31所述的方法，其中通过以下方程式计算由所述RMS检测器输出的所述经校正RMS值：
RMS(l)＝(1-αd(l))·RMS(l-1)+αd(l)·Rmin(l)
其中RMS(l)为所述经校正RMS值，且RMS(l-1)为前一经校正RMS值，αd(l)表示由正规化平滑因子计算器确定的样本l的所述正规化平滑因子，且Rmin为由所述最小rms值跟踪器所确定的所述最小rms值。
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