CN100382141C - 抑制风噪声的系统和方法 - Google Patents
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Abstract
一种语音增强逻辑提高了处理语音的感知质量。该语音增强系统包括一个噪声检测器和一个噪声衰减器。噪声检测器通过风冲击的建模来检测风冲击和连续噪声。噪声衰减器衰减风冲击，以提高清音、全音或混音段的清晰度。
Description
发明领域
本发明涉及声学，尤其是涉及一个提高处理声音的感知质量的系统。
发明背景
一些免提通信设备捕捉、吸取及传送语音信号。语音信号通过通信介质从一个系统传递到另一个系统中。包括车辆中所使用的一些系统中，语音信号的清晰度并不依赖于通信系统的质量或通信介质的质量。当噪声出现在声源或接收器附近时，所导致的失真使语音信号混乱，破坏信息，且在某些情况下，屏蔽了语音信号，致使收听者识别不出它。
令人厌烦、使人不能集中注意力的或导致信息丢失的噪声可能来源于许多声源。车辆内的噪声可能是由发动机、道路、轮胎或空气的流动而产生。在宽频率范围内可听到空气的自然流动或人工流动。振幅和频率的连续波动使得克服噪声变得困难，且降低了语音信号的清晰度。
许多系统试图消除风噪声的影响。一些系统依赖于遍布内部的各种抑制和消减声音的材料，以确保一个安静而舒适的环境。其它系统则试图平衡因风而引起的压在接收器上的变化的压力。这些噪声减压器可具有多种形式来滤掉所选择的压力，这使得对于车辆的许多内部很难设计。一些语音增强系统的另一个问题是在连续噪声背景中检测风噪声的问题。还有，一些语音增强系统的又一个问题是，它们不易适应其它对风噪声敏感的通信系统。
因此，需要有这样一个系统，能消除变化频率范围内的风噪声。
发明内容
语音增强逻辑提高了处理语音的感知质量。本系统学习、编码、然后抑制来自于输入信号的与空气流动有关的噪声。该系统包括一个噪声检测器和一个噪声衰减器。噪声检测器通过建模来检测一个风冲击，然后噪声衰减器消减这个风冲击。
另一种语音增强逻辑包括时间频率变换逻辑、背景噪声估计器、风噪声检测器和风噪声衰减器。时间频率变换逻辑将时间变化输入信号转换成频域输出信号。背景噪声估计器测量伴随输入信号的连续噪声。风噪声检测器自动识别及建模一个风冲击，然后由风噪声衰减器对其进行衰减。
对于本领域的技术人员，通过查看附图和详细描述，本发明的其它系统、方法、特点和优势将是，或将变得显而易见。所有这些附加系统、方法、特点和优势都要被包含在该说明中，包含在本发明的范围内，并且受所附权利要求的保护。
附图说明
参考附图和描述，可较好地理解本发明。图中的各个部分并不一定按照比例，重点放在说明本发明的原则上。此外，在图中将相同的参考数字指定给不同视图中的相应部分。
图1是语音增强逻辑的部分框图。
图2是与频域内的风和其它声源相关的噪声。
图3是与频域内的风和其它声源相关的噪声的信噪比。
图4是图1中语音增强逻辑的框图。
图5是一个与图1中语音增强逻辑相耦合的预处理系统。
图6是一个与图1中语音增强逻辑相耦合的另一种预处理系统。
图7是另一种语音增强系统的框图。
图8是与频域内的风和其它声源相关的噪声。
图9是一个屏蔽一部分语音信号的风冲击图。
图10是一个处理与重构的语音信号图。
图11是语音增强的流程图。
图12是语音增强的部分顺序图。
图13是语音增强的部分顺序图。
图14是车辆内的语音增强逻辑的框图。
图15是与音频系统和/或通信系统接口的语音增强逻辑的框图。
具体实施方式
语音增强逻辑提高了处理语音的感知质量。该逻辑可实时或延迟地自动学习和编码与空气流动相关的噪声的形状和方式。通过跟踪所选属性，该逻辑利用临时存储噪声的所选属性的有限内存，可消除或衰减风噪声。另外，该逻辑还可衰减连续噪声和/或“音乐噪声”、叽叽声、嘎嘎声、啾啾声、卡嗒声、滴答声、劈啪声、低频率乐音，或一些语音增强系统产生的其它人工声音。
图1是语音增强逻辑100的部分框图。该语音增强逻辑包含能在一个或多个处理器以及一个或多个操作系统上运行的硬件或软件。高度便携式逻辑包括一个风噪声检测器102和一个噪声衰减器104。
在图1中，风噪声检测器102可根据空气特征识别并建模与风流动相关的噪声。当风噪声在宽频范围内自然出现或人工产生时，风噪声检测器102的配置使其检测并建模人耳察觉到的风噪声。风噪声检测器接收输入的声音在短期频谱内可分为三类大致的范畴：(1)清音，表现出包括与风相关的噪声的类似噪声的特征，也就是说，它具有某一频谱形状，但不具有谐波或共振峰结构；(2)全音，表现出规则谐波结构，或在被描述共振峰结构的频谱包络所加权的基音谐波处达到峰值；和(3)混音，表现出上面两类的混合，一些部分包含类似噪声段，其余部分则表现出规则谐波结构和/或共振峰结构。
无论输入段多复杂或多大声，风噪声检测器102都可实时或延迟地从剩余信号中分离出类似噪声段。然后分析分离的类似噪声段，检测风噪声的出现，而在一些情况下，检测连续基础噪声的出现。当检测到风噪声时，就建模频谱，并且把该模型保留在内存中。当风噪声检测器102能够存储风噪声信号的整个模型时，它也可以将所选择的属性存储在内存中。
为了克服风噪声影响，及在一些情况下，为了克服包括环境噪声的基础连续噪声，噪声衰减器104从清音和混音信号中大体去除或衰减风噪声和/或连续噪声。语音增强逻辑100包含任何大体去除或衰减风噪声的系统。可衰减或去除风噪声的系统的例子包括利用信号和噪声估计的系统，诸如：(1)利用噪声信号的神经网络映像和对噪声抑制信号的噪声估计的系统，(2)从噪声信号中减去噪声估计的系统，(3)利用噪声信号和噪声估计从代码本中选择噪声抑制信号的系统，(4)任何利用噪声信号和噪声估计，基于屏蔽信号的重构，来创建噪声抑制信号的系统。这些系统可衰减风噪声，且在一些情况下，可衰减属于短期频谱部分的连续噪声。噪声衰减器104还可接口或包括一个可选的去除或衰减人工处理信号的剩余衰减器106。该剩余衰减器106可去除“音乐噪声”、叽叽声、嘎嘎声、啾啾声、卡嗒声、滴答声、劈啪声、低频率乐音，或其它人工声音。
图2示意与三种风流相关的典型噪声。风冲击202、204和206是风击打检测器的情况，它们随着击打力度和幅度的不同而变化。幅度反映出接收器或检测器的输入区内所受到的气压波动之间的力度或强度上的相对差别。风冲击下面的线表示也可被接收器或检测器感应的连续噪声208。车辆中的风冲击表示通过窗户、敞篷车的敞开顶部、进气孔的自然流动的空气，或表示由风扇或暖气、排气扇和/或空调系统(HVAC)而引起的人工流动的空气。连续噪声可表示环境噪声或与发动机、电力火车、道路、轮胎或其它声音相关的噪声。
在时间和频谱域中，连续噪声208和风冲击202可以是曲线。连续噪声和风冲击可能显现出图2中所示曲线的形式或特征。然而，当风冲击(例如σWB)的信号强度(以分贝为单位)与信噪比(SNR)域内连续噪声(例如σCN)的信号强度相关时，风冲击202具有线性函数的特征，垂直轴对应于分贝，水平轴对应于频率。该关系表达为
SNR＝σWB-σCN (等式1)
任何方法可近似于风冲击的线性。在信噪比域内，偏移或Y截距302和X截距或支点表示线性模型302的特征。可选择的是，X或Y坐标和斜率可建模风冲击。图3中，线性模型302以负斜率下降。
图4是一个可接收或检测清音、全音或混音输入信号的风噪声检测器102实例的框图。收到的或检测到的信号以预定频率进行数字化。为了确保一个高质量语音，具有任何公共采样率的模-数转换器402(ADC)把语音信号转换为脉冲编码调制(PCM)信号。平滑窗口404应用于数据块，以获取窗口信号。窗口信号的复合频谱可通过快速傅立叶变换(FFT)406的方式获得，该变换把数字信号分离成多个频率箱，每个箱识别小频率范围内的振幅和相位。然后把每个频率箱转换为功率频谱域408和对数域410，以产生一个风冲击和连续噪声估计。当声音的多窗口得到处理时，风噪声检测器102可导出平均噪声估计。可以使用时间平滑或加权平均来估计每个频率箱的风冲击和连续噪声估计。
为了检测风冲击，可给SNR域中选择部分的低频谱拟合一条直线。通过回归，一条最佳适配线可测量一个给定数据块内风噪声的强度。适配线与低频谱间的高度相关可识别一个风冲击。无论高度相关是否存在，它都依赖于处理声音的期望清晰度和风冲击的频率和振幅的变化。可选择的是，当适配线的偏移或Y截距超过一个预定阈值(例如，大于3分贝)时，可识别到一个风冲击。
为了限制语音屏蔽，对可疑风冲击信号的线的拟合可用规则来约束。示例性规则可阻止风冲击模型中的计算偏移、斜率或坐标点超过一个平均值。当检测到一个元音或另一个谐波结构时，另一个规则可阻止风噪声检测器102使用计算风冲击校正。谐波可通过其窄带宽和陡峰值，或结合语音或语调检测器来识别。如果检测到一个元音或另一个谐波结构，风噪声检测器就限制风冲击校正值为小于或等于平均值的值。一个附加规则允许平均风冲击模型或其属性仅在清音段被更新。如果检测到语音或混音段，平均风冲击模型或其属性在这个规则下不被更新。如果没有检测到语音，可通过任何方式来更新风冲击模型或每个属性，如通过加权平均或漏斗积分器。一些其它规则也可应用于该模型。这些规则给可疑风冲击提供了极佳线性拟合，而不需要屏蔽语音段。
为了克服风噪声影响，可通过任何方法使用噪声衰减器104从噪声频谱中彻底去除或衰减风冲击。一种方法将风冲击模型添加到记录或建模连续噪声中。在功率频谱中，然后把建模噪声从未修改频谱中减去。如果基础波峰或波谷902被风冲击202屏蔽，如图9所示，或被连续噪声屏蔽，传统的或改进的插值法可用于重构波峰和/或波谷，如图10所示。线性或逐步插补器用于重构信号的丢失部分。然后使用逆向FFT将信号功率转换为时间域，提供一个重构语音信号。
为了最小化“音乐噪声”、叽叽声、嘎嘎声、啾啾声、卡嗒声、滴答声、劈啪声、低频率乐音，或由一些风噪声衰减器产生的低频范围内的其它人工声音，在语音信号转换为时间域之前，可选的剩余衰减器106(图1中所示)还可调节语音信号。剩余衰减器106跟踪低频范围(例如，大约小于400HZ))内的功率谱。当检测到信号功率大幅增长时，可通过将低频范围内的发送功率限制或衰减到一个预定或计算阈值来获得改进。计算阈值等于或基于较早时期的相同低频范围的平均频谱功率。
通过在风噪声检测器处理输入信号之前预调节输入信号，可获得语音质量的进一步提高。一个预处理系统探索信号到达图5中所示分开放置的不同检测器时的滞后时间。如果使用多检测器或麦克风502将声音转换为电子信号，预处理系统就可包括自动选择感应最少数量噪声的麦克风502和信道的控制逻辑504。当选择了另一个麦克风502时，电信号在被风噪声检测器102处理之前与原先产生信号相混合。
可选择的是，多风噪声检测器102可以用来分析图6中所示的每个麦克风502的输入。可在每个信道中完成频谱风冲击估计。一个或多个信道的混合通过在麦克风502的输出之间交换来进行。在顺次排列的频率的基础上估计并选择信号，直到达到支点304(如图3所示)的频率为止。可选择的是，控制逻辑602通过加权函数在特定频率或频率范围内混合多个风噪声检测器102的输出信号。当超过支点的频率时，进程可继续进行或使用标准自适应形成方法。
图7是另一个也能提高处理声音的感知质量的语音增强逻辑700。这一提高可由将时间变化信号数字化并转换为频域的时间-频率变换逻辑702来完成。背景噪声估计器704测量出现在声源或接收器附近的连续或环境噪声。背景噪声估计器704包括一个平均每个频率箱内声音功率的功率检测器。为了阻止瞬时变化的有偏噪声估计，瞬变检测器706在功率的异常或不可预测增加时取消噪声估计过程。图7中，当瞬时背景噪声B(f，i)超过平均背景噪声B(f)AVE大于一个选定分贝级“C”时，瞬变检测器706禁止背景噪声估计器704。这一关系可表示为：
B(f，i)＞B(f)AVE +C (等式2)。
为了检测风冲击，风噪声检测器708为SNR域内频谱的选定部分拟合一条直线。通过回归，一条最佳适配线可建模风噪声202的强度，如图8所示。为了限制语音的任何屏蔽，对可疑风冲击线的拟合可由上文描述的规则来约束。当适配线的偏移量或Y轴截距超过一预定阈值时或当拟合线和与风冲击相关的噪声之间存在一个高度相关时，就可识别出风冲击。无论高度相关是否存在，都依赖于处理声音的期望清晰度及风冲击的频率和振幅的变化。
可选择的是，可通过分析显示在频谱仪上的输入信号的时间变化频谱特征，来识别风冲击。频谱仪可产生一个二维图形，被称作频谱图，其垂直轴对应频率，水平轴对应时间。
信号鉴别器710实时或延迟地标记频谱中的语音和噪声。可使用任何方法辨别语音和噪声。图7中，语音信号的识别可通过(1)其频带的窄带宽或波峰；(2)谐波相关的谐振结构；(3)与共振峰频率相对应的谐振或宽波峰；(4)随时间相对缓慢变化的特征；(5)它们的持续时间；和当使用多检测器或麦克风时，(6)检测器或麦克风的输出信号的关联。
为了克服噪声的影响，可通过任何方法使用风噪声衰减器712衰减或从噪声中彻底去除风冲击。一种方法将大体线性风冲击模型添加到记录或建模连续噪声中。然后在功率频谱中，通过上文所述的方法从未修改的频谱中去除建模噪声。如果基础波峰或波谷902被风冲击202屏蔽，如图9所示，或被连续噪声屏蔽，就使用传统的或改进的插值法重构波峰和/或波谷，如图10所示。线性或逐步插补器用于重构信号的丢失部分。然后利用时间序列合成器将信号功率转换为时间域，提供一个重构语音信号。
为了最小化“音乐噪声”、叽叽声、嘎嘎声、啾啾声、卡嗒声、滴答声、劈啪声、低频率乐音，或由一些风噪声衰减器产生的低频范围内的其它人工声音，还可使用一个替代的剩余衰减器714。该剩余衰减器714跟踪低频范围内的功率频谱。当检测到信号功率的大幅度增加时，通过将低频范围内的发送功率限制在一个预定或计算阈值，来获得改进。计算阈值等于或基于较早时期的相同低频范围的平均频谱功率。
图11是语音增强的流程图，去除一些风冲击和连续噪声以提高处理语音的感知质量。在动作1102，以预定频率对收到的或检测到的信号进行数字化。为确保好的语音质量，ADC把语音信号转换为PCM信号。在动作1104，通过FFT的方式可获得窗口信号的复合频谱，该FFT将数字信号分离到各个频率箱中，每个箱识别小频率范围的振幅和相位。
在动作1106，检测连续或环境噪声。背景噪声估计包括每个频率箱中声音功率的平均值。为了防止瞬间的有偏噪声估计，在动作1108，在功率异常或不可预测的上升时终止噪声估计进程。当瞬时背景噪声超过平均背景噪声大于一个预定分贝级时，瞬变检测动作1108取消背景噪声估计。
在动作1110，当偏移量超过预定阈值(例如，阈值大于3分贝)或当最佳拟合线与低频频谱间的高度相关退出时，可检测到风冲击。可选择的是，通过分析输入信号的时间变化频谱特征，来识别风冲击。当使用直线拟合检测方法时，可疑风冲击信号的拟合线受一些可选动作的约束。示例可选动作阻止风冲击模型中的计算偏移、斜率或坐标点超过一个平均值。当检测到一个元音或另一个谐振结构时，另一可选动作阻止风噪声检测法使用计算风冲击校正。如果检测到元音或另一个谐振结构时，风噪声检测法将风冲击校正值限定到小于或等于平均值。一个附加可选动作允许平均风冲击模型或属性仅在清音段被更新。如果检测到语音或混音段，则在这一动作下不更新平均风冲击模型或属性。如果没有检测到语音时，则可通过如加权平均或漏斗积分器等许多方式对风冲击模型或每个属性进行更新。许多其它可选动作也可应用于该模型。
在动作1112，信号分析可鉴别或标记来自类似噪声段的语音信号。可通过以下方法进行语音信号的识别，例如，(1)其频带的窄带宽或波峰；(2)谐波相关的谐振结构；(3)与共振峰频率相对应的谐波；(4)随时间相对缓慢变化的特征；(5)它们的持续时间；和当使用多检测器或麦克风时，(6)检测器或麦克风的输出信号的关联。
为了克服风噪声的影响，利用任一动作从噪声频谱中彻底去除或衰减风噪声。一个示例动作1114将大体线性风冲击模型添加到记录或建模连续噪声中。然后在功率频谱中，由上文所述的方法和系统从未修改频谱中彻底去除建模噪声。如果基础波峰或波谷902被风冲击202屏蔽，如图9所示，或被连续噪声屏蔽，就在动作1116使用传统的或改进的插值法重构波峰和/波谷。然后在动作1120使用时间序列合成将信号功率转换成时间域，提供一个重构语音信号。
为了最小化“音乐噪声”、叽叽声、嘎嘎声、啾啾声、卡嗒声、滴答声、劈啪声、低频率乐音，或由一些风噪声衰减器产生的低频范围内的其它人工声音，在将信号转化回时间域之前，也可执行剩余衰减方法。一可选的剩余衰减方法1118跟踪低频范围内的功率频谱。当检测到信号功率的大幅度上升时，可通过将低频范围的发送功率限制到一个预定或计算阈值，来获得改进。计算阈值等于或基于较早时期的相同低频范围的平均频谱功率。
图12和图13是语音增强的部分序列图。如图11中所示的方法一样，在信号承载介质(诸如存储器的计算机可读介质)中，对序列图进行编码，在诸如一个或多个集成电路内对其进行编程，或由控制器或计算机进行处理。如果利用软件执行这些方法，该软件就驻留在常驻或接口到风噪声检测器102的存储器、通信接口、或其它所有类型的接口到或常驻在语音增强逻辑100或700的非易失性或易失性存储器中。存储器包括执行逻辑功能的可执行指令的有序列表。逻辑功能可通过数字电路、源代码或通过模拟电子、音频或视频信号的模拟源来实现。软件可以包含在任何计算机可读或信号承载介质中，该介质被指令可执行系统、仪器或设备使用或与其连接。这样的系统包括基于计算机的系统、包含处理器的系统、或从可执行指令的指令可执行系统、仪器或设备有选择地读取指令的其它系统。
一个“计算机可读介质、“机器可读介质、“传播信号介质”和/或“信号承载介质”包括许多方式，这些方式包含、存储、通信、传播或传输指令可执行系统、仪器或设备所使用的与其连接的软件。机器可读介质可选择地，是但并不局限于，电子、磁性、光学、电磁、红外或半导体系统、仪器、设备或传播介质。机器可读介质的非穷尽列举表的实例包括：具有一根或多根导线的电连接的“电子设备”、便携式磁盘或光盘，如随机存取存储器“RAM”(电子的)的易失性存储器、只读存储器“ROM”(电子的)、可檫可编程只读存储器(EPROM或闪存)(电子的)或光纤(光学的)。机器可读介质还可包括在其上面打印有软件的有形介质，如同软件作为一个图像或以另一种格式(例如，通过光扫描)被电子存储，然后被编译，并且/或者被解释或作其它的处理。处理的介质然后被存储在计算机和/或机器存储器中。
如图12的第一个序列所示，时间序列信号可由汉宁窗(HanningWindow)进行数字化并平滑，以提供全音、混音或清音段的准确估计，窗信号的复合频谱通过FFT方式来获得，FFT把数字信号分离到各个频率箱中，各个箱识别小频率范围内的振幅。
在第二个序列中，清音段的每个频率箱中的平均声音功率导出背景噪声估计。为了阻止有偏噪声估计，当检测到异常或不可预测的功率波动时，噪声估计就不发生。
在第三个序列中，未修改频谱由窗口进行数字化、平滑，并由FFT变换成复合频谱。未修改频谱显示出包含类似噪声段的部分以及其它表明规则谐振结构的部分。
在第四个序列中，声音段被拟合成分离线，以建模风和连续噪声的强度。为提供较完整的解释，示意了清音、全音和混音样本。每个样本的频率箱被转换成功率频谱域和对数域，以探索风冲击和连续噪声估计。随着较多窗口的处理，就可以推导出平均风噪声和连续噪声估计。
为了检测风冲击，给SNR域内的信号的选定部分拟合一条直线。通过回归，最佳适配线建模每个示例中风噪声的强度。一个最佳适配线与低频谱之间的高度相关可识别风冲击。可选择的是，超过预定阈值的Y截距也可识别风冲击。为了限制语音的屏蔽，对可疑风冲击信号的线拟合受到上文所述规则的约束。
为了克服风噪声的影响，可在未更改频谱中衰减建模噪声。图13中，来自清音和混音样本的风冲击和连续噪声的衰减示意在第五个序列中。将信号功率转换为时间域的反向FFT提供重构语音信号。
从前面描述很明显可以看出，上文所述的系统可调节仅从一个麦克风或检测器所接收的信号。显然，许多系统的组合也可用于识别及跟踪风冲击。除了对可疑风冲击拟合线之外，一个系统可(1)检测具有大于预定阈值的SNR的频谱的波峰；(2)识别宽度大于预定阈值的波峰；(3)识别缺乏谐波关系的波峰；(4)把波峰与先前语音频谱进行比较；(5)在区分风冲击段、其它类似噪声段和规则谐振结构之前，比较从不同麦克风检测到的信号。上文所述的一个或多个系统也可应用在可选择的语音增强逻辑中。
其它可选择语音增强系统包括上文所述结构和功能的组合。这些语音增强系统由上文所述的或附图中示意的结构和功能的任意组合构成。逻辑可在硬件或软件中实现。术语“逻辑”广义上可包括硬件设备或电路、软件或其组合。硬件包括处理器或具有易失性和/或非易失性存储器的控制器，还可包括通过无线和/或硬件介质连接外围设备的接口。
语音增强逻辑方便适应任何技术或设备。如图14所示的一些语音增强系统或组件接口或耦合车辆，将语音和其它声音转换为可传送到远方的一种形式的设备，如图15所示的有线和无线电话及音频设备，以及其它对风噪声敏感的通信系统。
语音增强逻辑提高了处理语音的感知质量。逻辑可实时或延迟地自动学习和编码与空气的流动相关的噪声的形状和形式。通过跟踪所选择的属性，利用临时或永久存储风噪声的所选属性的有限存储器，逻辑可以消除或衰减风噪声。语音增强逻辑还可消减连续噪声和/或叽叽声、嘎嘎声、啾啾声、卡嗒声、滴答声、劈啪声、低频率乐音或其它一些语音增强系统内产生的人工声音，并且在必要时重构语音。虽然已经描述了本发明的多种实施例，但对本领域内的普通技术人员来说，很明显本发明范围还可以包括更多实施方案和实例。因此，除了受所附权利要求及其等同文件的限定之外，本发明不受任何限制。
Claims (35)
1.一种抑制语音或清音信号中风噪声的系统，其包括：
一个第一噪声检测器，它被配置成通过建模来检测来自输入信号的风冲击；和
一个噪声衰减器，它与噪声检测器电连接，用来在很大程度上去除输入信号中的风冲击。
2.如权利要求1所述的风噪声抑制系统，其中所述第一噪声检测器对部分的输入信号建模一条线。
3.如权利要求2所述的系统，其中配置所述第一噪声检测器，使其对信噪比域中的输入信号的一部分拟合一条线。
4.如权利要求1所述的系统，其中配置所述第一噪声检测器，使其通过计算信号的偏移量来对风冲击建模。
5.如权利要求1所述的系统，其中配置所述第一噪声检测器，使其阻止已建模风冲击的属性超过其各自的平均值。
6.如权利要求1所述的系统，其中配置所述第一噪声检测器，使其在检测到元音或谐波类似结构时，限制风冲击的校正。
7.如权利要求1所述的系统，其中配置所述第一噪声检测器，使其导出一个平均的风冲击模型，当检测到语音或混音信号时，将不更新该平均风冲击模型。
8.如权利要求1所述的系统，其中配置所述第一噪声检测器，使其导出一个平均的风冲击模型，该模型是通过较早分析的其它建模信号的加权平均得到的。
9.如权利要求1所述的系统，其中配置噪声衰减器，使其在很大程度上去除输入信号中的风冲击和连续噪声。
10.如权利要求1所述的系统，还包括一个与所述第一噪声检测器和噪声衰减器导电耦合的剩余衰减器，当检测到低频范围内信号功率的大幅增长时，该剩余衰减器衰减低频范围内的信号功率。
11.如权利要求1所述的系统，还包括一个与所述第一噪声检测器导电耦合的输入设备，该输入设备的配置使得声波转换为模拟信号。
12.如权利要求1所述的系统，还包括一个与所述第一噪声检测器耦合的预处理系统，该预处理系统的配置使其在所述第一噪声检测器处理输入信号之前，预处理该输入信号。
13.如权利要求12所述的系统，其中预处理系统包括空间上隔开的第一麦克风和第二麦克风，并且其配置使得可以利用到达不同检测器的信号的延迟时间。
14.如权利要求13所述的系统，还包括控制逻辑，它自动选择感应输入信号中最少数量噪声的一个麦克风和一个声道。
15.如权利要求13所述的系统，还包括一个与所述第一噪声检测器和第一麦克风耦合的第二噪声检测器。
16.一种检测语音和清音信号中噪声的系统，其包括：
一个时频变换逻辑，它将时变输入信号转换成频域；
一个与时频变换逻辑耦合的背景噪声估计器，该背景噪声估计器的配置使其可以测量出现在接收器附近的连续噪声；和
一个与背景噪声估计器耦合的风噪声检测器，该风噪声检测器被配置成通过建模而自动识别与风相关的噪声。
17.如权利要求16所述的系统，还包括一个瞬变检测器，其配置使得当检测到瞬变信号时，禁止背景噪声估计器。
18.如权利要求16所述的系统，其中风噪声检测器的配置使得导出拟合线与一部分输入信号之间的关系。
19.如权利要求16所述的系统，还包括一个与风噪声检测器耦合的信号鉴别器，该信号鉴别器的配置使其可以标记输入信号的语音和噪声段。
20.如权利要求16所述的系统，还包括一个与风噪声检测器耦合的风噪声衰减器，该风噪声衰减器的配置使其可以减少与接收机感应到的风相关的噪声。
21.如权利要求16所述的系统，其中的噪声衰减器的配置使其在很大程度上去除与输入信号中的风相关的噪声。
22.如权利要求16所述的系统，还包括一个与背景噪声估计器耦合的剩余衰减器，当检测到低频范围内的信号功率大幅增长时，可使用它衰减低频范围内的信号功率。
23.一种抑制语音或清音信号中风噪声的系统，其包括：
一个时频变换逻辑，它将时变输入信号转换成频域；
一个与时频变换逻辑耦合的背景噪声估计器，该背景噪声估计器的配置使其可以测量出现在接收器附近的连续噪声；
一个与背景噪声估计器耦合的风噪声检测器，该风噪声检测器的配置使其对一部分输入信号拟合一条线；和
一个与风噪声检测器方法耦合的风衰减器，该风衰减器的配置使其可以去除与接收器感应到的风相关的噪声。
24.一种衰减输入信号中的风冲击的方法包括：
将时变信号转换为复谱；
估计背景噪声；
当拟合线与一部分输入信号之间存在高度相关性时，检测风冲击；和
衰减输入信号的风冲击。
25.如权利要求24所述的方法，其中估计背景噪声的动作包括，当没有检测到瞬变时，估计背景噪声。
26.如权利要求24所述的方法，其中风冲击的衰减动作包括，在很大程度上去除输入信号的风冲击。
27.一种去除输入信号中风冲击的方法包括：
将时变信号转换为复谱；
估计背景噪声；
当拟合线与一部分输入信号之间存在高度相关性时，检测风冲击；和
去除输入信号的风冲击。
28.一种检测风噪声的方法，包括：
将声波转换为电信号；
以频谱方式将电信号从第一个域转换为第二个域；和
建模与风相关的一部分声波。
29.如权利要求28所述的方法，还包括导出一部分被噪声屏蔽的语音信号的逻辑。
30.如权利要求28所述的方法，还包括衰减部分声波。
31.如权利要求28所述的方法，还包括限制低频范围内的功率。
32.如权利要求28所述的方法，还包括测量由检测器感应到的连续或环境噪声。
33.如权利要求32所述的方法，还包括当检测到功率增大时禁用估计逻辑。
34.如权利要求28所述的方法，还包括使一条线与所述电信号的一部分相关，以检测风中的风冲击。
35.如权利要求28所述的方法，其中建模与风相关的一部分声波包括仅建模与风相关的声波。
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- 2004-02-18 DE DE602004001694T patent/DE602004001694T2/de not_active Expired - Lifetime
- 2004-02-19 JP JP2004043727A patent/JP2004254322A/ja not_active Ceased
- 2004-02-20 KR KR1020040011353A patent/KR101034831B1/ko active IP Right Grant
- 2004-02-21 KR KR1020040011708A patent/KR101045627B1/ko active IP Right Grant
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| CN111521406B (zh) * | 2020-04-10 | 2021-04-27 | 东风汽车集团有限公司 | 一种乘用车道路测试高速风噪分离方法 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1530929A (zh) | 2004-09-22 |
| EP1450353B1 (en) | 2006-08-02 |
| US20040167777A1 (en) | 2004-08-26 |
| KR20040075787A (ko) | 2004-08-30 |
| KR20040075771A (ko) | 2004-08-30 |
| EP1450353A1 (en) | 2004-08-25 |
| CA2458428C (en) | 2012-05-15 |
| KR101034831B1 (ko) | 2011-05-17 |
| US20110026734A1 (en) | 2011-02-03 |
| DE602004001694T2 (de) | 2006-11-30 |
| KR101045627B1 (ko) | 2011-07-01 |
| JP2004254322A (ja) | 2004-09-09 |
| US8165875B2 (en) | 2012-04-24 |
| DE602004001694D1 (de) | 2006-09-14 |
| CA2458428A1 (en) | 2004-08-21 |
| US7895036B2 (en) | 2011-02-22 |
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