CN106797512B - 多源噪声抑制的方法、系统和非瞬时计算机可读存储介质 - Google Patents

多源噪声抑制的方法、系统和非瞬时计算机可读存储介质 Download PDF

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CN106797512B
CN106797512B CN201580045305.7A CN201580045305A CN106797512B CN 106797512 B CN106797512 B CN 106797512B CN 201580045305 A CN201580045305 A CN 201580045305A CN 106797512 B CN106797512 B CN 106797512B
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CN106797512A (zh
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S·K·内马拉
J·伍德拉夫
T·韦尔马
F·考德威尔
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Merchant Building Electronics Co Ltd
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/20—Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/06—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being correlation coefficients
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/28—Constructional details of speech recognition systems
    • G10L15/30—Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L2021/02082—Noise filtering the noise being echo, reverberation of the speech
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02166—Microphone arrays; Beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/005—Circuits for transducers, loudspeakers or microphones for combining the signals of two or more microphones

Abstract

多源噪声抑制的方法、系统和非瞬时计算机可读存储介质。提供了用于多源的噪声抑制的系统和方法。示例系统可以接收包括语音信号和噪声的音频数据流，所述语音信号包括口语词语。音频数据流由分布式音频设备提供。系统可以至少部分基于音频流的质量向音频流分配权重。可以基于信噪比(SNR)来确定音频流的权重。系统可以基于权重进一步处理音频流以生成干净的语音。每个音频设备包括麦克风，并且可以与物联网(IoT)相关联，使得音频设备是物联网设备。所述处理可以包括噪声抑制和减少以及回波消除。可以将干净的语音提供给远程设备用于进一步处理，所述进一步处理可以包括自动语音识别(ASR)。

Description

多源噪声抑制的方法、系统和非瞬时计算机可读存储介质
相关申请的交叉引用
本申请要求于2014年8月28日提交的美国临时申请No.62/043,344的权益。出于所有目的，上述申请的主题通过引用并入本文中。
技术领域
本申请大体上涉及音频处理，并且更具体地，涉及用于提供多源噪声抑制的系统和方法。
背景技术
自动语音识别(ASR)和语音用户界面(VUI)被广泛地用于控制不同类型的设备(诸如，电视机、游戏机等)。通常，当用户位于设备附近(例如，在与设备相同的房间中)时，用户发出语音命令以控制设备。然而，如果用户需要为位于不同房间、车库、不同房子或另一远程位置的设备提供语音命令，则这样的位置可能是不方便的。此外，由于设备操作的噪声环境而导致语音命令可能不清楚。因此，设备可能无法识别所发出的命令。因此，可能需要用于将口头命令传递到具有VUI界面的设备的更鲁棒的系统和方法。
发明内容
提供本发明内容是为了以简化形式引入下面在详细说明书中进一步描述的构思的选择。本发明内容不旨在标识要求保护的主题的关健特征或者必要特征，也不旨在用于确定要求保护的主题的范围的辅助。
描述了用于多源音频处理的系统和方法。一种用于多源噪声抑制的示例性方法包括以下步骤：向音频流分配权重，所述音频流由多个音频设备基本上同步地提供，所述权重取决于所述音频流的质量；基于所述权重处理所述音频流以生成干净的语音信号；以及将所述干净的语音信号提供给至少一个远程设备用于进一步处理。在一些实施方式中，每个音频设备包括至少一个麦克风，并且与物联网(本文也称为物联网设备)相关联。
本公开的其它示例实施方式和方面将从结合以下附图进行以下描述变得显而易见。
附图说明
实施方式借助于示例来示出，但是并不限于附图，在附图中，相同的标记指示相似的元件。
图1是示出可以实践多源噪声抑制的方法的示例性环境的框图。
图2是示出根据示例性实施方式的音频设备的框图。
图3是示出根据示例性实施方式的用于多源噪声抑制的系统的框图。
图4是示出根据示例性实施方式的用于多源噪声抑制的方法的流程图。
图5是示出实现所公开的技术的实施方式的示例性计算系统的框图。
具体实施方式
本文中所公开的技术涉及用于多源噪声抑制(本文中也称为基于群的噪声抑制)的系统和方法。可以利用被配置为至少捕获声学信号的多个音频设备来实践本技术的各种实施方式。音频设备可以包括蜂窝电话、智能电话、可穿戴设备、平板电脑、平板手机、摄像机、电话听筒、耳机、会议系统以及具有一个或更多个麦克风和捕获声音的功能的其它设备。在一些实施方式中，音频设备是连接到物联网(IoT)或物联网的一部分(例如，全球连接的设备的动态网络)的设备，所述设备可以包括通常不被认为是音频设备(诸如，智能恒温器、智能家电等)的设备。
在各种实施方式中，音频设备还包括射频(RF)接收器、发射器和收发器、有线和/或无线电信和/或联网设备、放大器、音频和/或视频播放器、编码器、解码器、扬声器、输入端、输出端、存储设备和用户输入设备。音频设备还可以包括输入设备(诸如，按钮、开关、按键、键盘、轨迹球、滑块、触摸屏、一个或更多个麦克风、陀螺仪、加速计、全球定位系统(GPS)接收器等)。音频设备还可以包括输出端(诸如，LED指示器、视频显示器、触摸屏、扬声器等)。
在各种实施方式中，音频设备在固定和便携式环境中操作。固定环境包括住宅和商业建筑物或结构等。例如，固定实施方式包括客厅、卧室、家庭影院、会议室、礼堂、商业场所等。便携式环境包括移动车辆、移动人员、交通工具等。
本技术可以用于向设备(诸如，位于房子的不同部分中、在车辆中或在另一房子中的设备)提供远程命令。另外，本技术可以用于使得能够进行直播通信(live-talkcommunication)(即，与位于房子的不同部分或甚至在不同房子中的第二用户的实时通信)。在一些实施方式中，数据通过本地有线网络或本地无线网络(参见，例如网络140)或通过计算云160被中继到另一设备。
图1是示出根据示例性实施方式的、可以实践多源噪声抑制的方法的环境100的框图。示例环境100包括一个或更多个音频设备110。音频设备110可以位于住宅和/或办公室内的不同地方。每个音频设备110可以被配置为接收声学信号，处理所述声学信号以生成音频流，并将所述音频流发送到远程设备。在一些实施方式中，每个音频设备110可以包括用于捕获声学声音的至少一个麦克风。在各种实施方式中，声学信号可以包括来自用户120被一个或更多个噪声源130污染的声音。噪声源130可以包括街道噪声、环境噪声、和来自除了期望的扬声器120之外的实体的语音。例如，噪声源130包括工作空调、通风风扇、街道噪声、电视机、移动电话、立体声音频系统等。
在各种实施方式中，音频设备110经由网络140互连。在一些实施方式中，网络140包括本地网络(例如，Wi-Fi网络、蓝牙网络等)。另外或另选地，音频设备110可以经由有线网络或网状网络互连。在一些实施方式中，音频设备110可以包括控制器/协调器150(在本文中也称为“控制器150”)。在特定实施方式中，音频设备110被同步到由外部设备或控制器150提供的公共时间源。控制器/协调器150可以是路由器、芯片、音频设备110(诸如，电视机)中的一个等。例如，如果音频设备110经由无线网络互连，则路由器可以充当控制器/协调器150。
在另外的实施方式中，音频设备110中的一个或更多个连接到基于云的计算资源160(在本文中也称为“计算云160”和“基于云的计算资源服务160”)。在一些实施方式中，基于云的计算资源包括一个或更多个服务器群/服务器群集，所述服务器群/服务器群集包括可以与网络交换机和/或路由器协同定位(co-locate)的计算机服务器的集合。基于云的计算资源160可以包括将音频设备110互连用于音频设备110之间的数据交换的应用以及用于处理从音频设备110、控制器150和其它服务接收的数据的应用。
在各种实施方式中，音频设备110恒定地或周期性地监听语音并缓冲音频数据。示例性音频设备110经由网络140彼此通信。在各种实施方式中，音频设备是连接到物联网或者物联网的一部分的设备。示例性音频设备100具有用于捕获声音的一个或更多个麦克风，并且可以连接到网络(例如，互联网)。这样的示例性音频设备在本文中也被称为“物联网设备”或“IoT设备”。借助于示例而非限制，第一和第二音频设备110可以位于距扬声器120(在本文中也称为说话人或用户120)不同的距离处。由第一和第二音频设备110捕获的音频数据可以被提供给控制器/协调器150，并且被视为来自主麦克风和次麦克风的数据。利用该信息，控制器150可以执行回波和噪声抑制。例如，当用户120在房子周围行走时，位于整个房子各处的另选音频设备110和麦克风可以变得对于从用户120拾取语音来说最佳。当用户120说话(例如，向音频设备110提供语音命令)时，所有收听的音频设备110和麦克风将其带时间戳的数据发送到控制器/协调器150用于进一步处理。
图2是示出各种实施方式中的适于实现多源噪声抑制的方法的示例性音频设备110的框图。示例音频设备110可以包括收发器210、处理器220、麦克风230、音频处理系统240和输出设备250。音频设备110可以包括更多组件或其它组件以提供特定的操作或功能。类似地，音频设备110可以包括更少的组件以执行类似于或等同于图2所描述的功能的功能。
在图2的示例中，收发器210被配置为与网络(诸如，互联网、广域网(WAN)、局域网(LAN)、蜂窝网络等)进行通信，以接收和/或发送音频数据流。接收到的音频数据流可以被转发到音频处理系统240和输出设备250。
处理器220可以包括实现音频数据的处理和根据音频设备110(例如，通信设备和计算机)的类型的各种其它操作的硬件、固件和软件。存储器(例如，非瞬时计算机可读存储介质)可以至少部分地存储用于由处理器220执行的指令和数据。
音频处理系统240可以包括实现声学信号的编码的硬件、固件和软件。例如，音频处理系统240还被配置为经由麦克风230(所述麦克风230可以是一个或更多个麦克风或声传感器)从声源接收声学信号并处理所述声学信号。在被麦克风230接收之后，声学信号可以通过模数转换器转换为电信号。
示例性输出设备250包括可以向听众(例如，声源)提供音频输出的任何设备。例如，示例性输出设备250包括扬声器、D类输出、头戴式耳机的听筒或音频设备110上的电话听筒(handset)。
图3是示出根据示例性实施方式的适于实现多源噪声抑制的方法的系统300的框图。示例系统300可以被合并在控制器150(图1所示)中，并且用于从一个或更多个音频设备110(图1所示)经由网络140(也在图1中示出)接收音频流。系统300可以包括加权模块310、噪声抑制和减少模块320、回波消除模块330和ASR模块340。在一些实施方式中，系统300的模块310-340被实现为存储在存储器中并由控制器/协调器150的处理器执行的指令。在其它实施方式中，系统300可以被实现为被并入控制器/协调器150中的硬件、芯片或固件。系统300可以在使用适于与物联网设备进行通信和在物联网设备中进行通信的协议的环境中操作。
在另外的实施方式中，系统300的模块310-340中的一些或全部可以被实现为在远程服务器上或者由基于云的计算资源服务160(也在图1中示出)存储和执行的指令。控制器150可以经由网络140将用于发送音频流和用于处理的其它数据的命令传送到计算云160，并且可以接收计算结果。
在各种实施方式中，控制器150可以用于执行分集汇集(diversity pooling)。也就是说，控制器150可以从N个音频设备110接收N个音频数据流。每个音频流可以包括语音信号和噪声。加权模块310可以执行基于由质量度量确定的音频数据的质量为每个接收的音频数据流分配权重的算法。在特定实施方式中，基于作为质量度量的信噪比来计算与音频流相关联的权重。音频数据的质量可以取决于相应音频设备110在其中操作的特定环境。因此，在特定实施方式中，分配给音频数据流的权重取决于音频设备110的环境条件。例如，如果用户120正在看电视，则位于用户120正上方的麦克风对于拾取用户的语音来说可以是最佳的。然而，如果麦克风位于加热、通风或空调(HVAC)系统附近，则由于在例如空调(AC)处于操作时的降低的信噪比而导致麦克风可能不是最佳的。因此，分配给来自麦克风的音频数据的权重可以取决于噪声源(诸如，本示例中的AC)是否激活。
在一些实施方式中，音频数据的质量和分配给音频数据的权重可以取决于相应音频设备110的组件的特定特性(例如，麦克风的类型、音频处理系统的类型等)。
在一些实施方式中，示例性系统300执行分布式噪声抑制和减少以将噪声与音频数据分离开并使用多音频流数据和分配给音频流数据的权重来提取干净的语音。例如，在具有多个麦克风的音频设备110中，主声学信号和辅声学信号的能量之间的麦克风间电平差(ILD)可以用于声学信号增强。例如，在标题为“System and Method for UtilizingInter-Microphone Level Differences for Speech Enhancement”的美国专利申请号11/343,524(美国专利号为8,345,890)中描述了用于声学信号增强的方法和系统，出于上述目的，其公开内容通过引用并入本文中。
另外，在一些实施方式中，通过使用多音频流数据和分配给所述音频流数据的权重，系统300可以执行各种其它处理(举几个例子来说，诸如，回波消除和增益控制)。关于应用加权以修改声学信号的另外的细节在标题为“Systems and Methods for Producing anAcoustic Field Having a Target Spatial Pattern”(美国专利号为8615392)的共同转让的美国专利申请号12/893,208中找到，并通过引用并入本文中。例如，随着用户120围着房子行走，并且随着环境条件改变，分配给来自每个音频设备110的每个音频流的权重被动态地调整，并且执行信号处理(增益控制、回波消除、噪声抑制等)以始终确保最佳的音频质量和语音识别。
所述方法的上述实施方式可以在IoT环境中操作。现在描述关于根据各种实施方式的在IoT环境中操作的方法的另外的细节。
在一些实施方式中，每个音频设备110包括至少一个麦克风，并且与物联网(本文也称为物联网设备或IoT设备)相关联。
在一些实施方式中，该方法(并且特别是加权)包括通过在单房间或多房间物联网环境中定位、识别和映射目标声音(例如，语音)和噪声源通过组合来自多个物联网设备(例如，音频设备110)上的麦克风的多个音频流来生成声学活动地图，以创建环境的多维声学视图。
可以使用IoT设备附近的声源在IoT设备之间持续更新声学签名。
听觉场景分析和场景分类器可以用于识别噪声和目标声音类型。关于示例性场景分析和场景分类器的另外的细节可以在标题为“Speech Signal Separation andSynthesis Based on Auditory Scene Analysis and Speech Modeling”的美国专利申请号14/335,850和标题为“Monaural Noise Suppression Based on ComputationalAuditory Scene Analysis”的美国专利申请号12/860,043(美国专利号为8,447,596)中找到，这两个文献通过引用并入本文中。在一些实施方式中，IoT设备之间的信令机制(包括发射器和接收器)用于识别IoT设备之间相对于彼此的位置。
在各种实施方式中，该方法包括基于声学活动地图来识别为讲话者(例如，用户120)提供良好的信噪比(SNR)的最佳音频设备，以及(IoT设备之中的)最佳音频设备的识别以测量讲话者的环境和周围环境中的噪声。该识别可以用于向与音频设备相关联的音频流分配权重。在各种实施方式中，来自音频设备的音频流的组合被用于增强目标信号的音频处理(例如，噪声消除、噪声抑制等)。作为结果，当讲话者(例如，用户120)在单个房间或跨不同房间四处移动时，各种实施方式提供无缝的、免提的语音通信体验。在另外的结果中，各种实施方式提供无论哪个IoT设备具有最佳SNR的优雅的、平滑的切换、以及无论哪个IoT设备具有最佳噪声测量的优雅的、平滑的切换。
此外，在一些实施方式中，该方法提供流畅(fluid)人机语音界面，所述流畅人机语音界面可以得到跨物联网环境中的多个IoT设备的高执行ASR。
另外，所述方法在特定实施方式中提供使IoT设备在最佳位置处、在最佳时间并且以最佳音量与用户120进行通信(例如，使用扬声器或IoT设备的其它通信功能)。因此，特定实施方式将提供正在监听和与用户120通信的多个IoT设备之间和所述多个IoT设备中的无缝切换。
在一些实施方式中，可以将所得到的干净的语音信号提供给ASR模块340，例如，以提取口头命令。在一些实施方式中，ASR模块340可以将远程设备360与口头命令(根据命令上下文，例如，电视、流送设备等)相关联，并将所述口头命令提供给相关联的远程设备360用于进一步处理。在其它实施方式中，干净语音用于各种语音界面和其它服务。
示例1.远程命令。
通过示例而非限制的方式，在一些实施方式中，用户120从音频设备110(图1所示)向一个设备提供语音命令，并且所述设备可以经由网络140将所述该命令中继给不同位置处的不同设备。语音命令可以被位于房屋(例如，房子)周围的各种音频设备110上或连接到房屋(例如，房子)周围的各种音频设备110的麦克风拾取并被发送到控制器/协调器150。控制器/协调器150可以包括路由器或诸如TV的设备。一旦控制器/协调器150接收到所述命令，它可以请求所有其它设备发送带时间戳的音频命令(以及用于上下文的先前音频的一小部分)。可以使用多麦克风数据来执行诸如为音频流加权、噪声抑制、回波消除、增益控制和ASR算法的执行的操作，以清理语音命令。可以在控制器150上或在计算云160上本地地执行数据处理。因此，当在本示例中用户120围绕房屋走动并发出语音命令时，命令被拾取、被处理并被发送到ASR模块340。
在一些实施方式中，用户120可以向位于房屋的其它区域(例如，房子的车库区域)中的设备发送远程命令。在其它实施方式中，如果有人试图启动车辆(例如，如果用户的十几岁的儿子正试图乘坐车辆)，则用户120可以向车辆发送远程命令或从车辆接收通知。
在另外的实施方式中，用户120可以向位于其它房屋(例如，诸如，由用户的年迈的父母拥有的第二个房屋)中的设备发送远程命令，在这种情况下，可以通过计算云中继所述命令。
示例2.直播通信。
本文所描述的技术还可以允许位于房屋的不同部分中的两个或更多个用户120之间或者各个房屋(例如，不同房子)中的用户之间的实时通信。
通过示例而非限制的方式，用户#1发出语音命令(诸如，“与我的爸爸连接”)，并且该命令可以被位于用户#1附近的各种音频设备110拾取。在各种实施方式中，如上面在示例1中所描述的，处理包含该命令的不同音频流以提取干净的语音并识别所述命令。在该示例中一旦一个或更多个控制设备理解了该命令，则建立音频设备110与位于用户#2(例如，爸爸)附近的一个或更多个设备之间的通信。用户#1和用户#2通过位于每个用户120附近的音频设备110之间所建立的通信链路进行谈话。来自用户#1的语音被用户#1附近的一个或更多个音频设备110接收，被处理以提取干净的语音，如本文所述，并被发送到用户#2(例如，用户的爸爸)附近的一个或更多个音频设备110。来自用户#2(例如，用户的爸爸)的语音可以类似地被用户#1处理和接收。
在一些实施方式中，如果用户#2位于同一房子中，则可以使用无线(例如，WiFi)或有线(例如，以太网)连接通过例如本地网络传送数据。在其它实施方式中，如果用户#2位于不同的房子中，则通过WAN或包括计算云环境的其它基础设施来发送数据。使用本文所描述的技术的足够的网络化音频设备110的布置可以使得用户120能够在用户120在整个房屋(例如，房子)中移动时连接到另一个人并与另一个人讲话。
图4是示出根据示例性实施方式的用于多源噪声抑制的方法400的流程图。示例方法400可以通过向音频流分配权重而开始于操作402。音频流可以由分布式音频设备110提供。音频流可以包含语音和噪声。在各种实施方式中，应用于音频流的权重例如使用信噪比基于音频流的质量来确定。在操作404处继续的处理基于分配给音频流的权重可以生成干净的语音。处理可以包括增益控制、噪声抑制、降噪、回波消除等。在操作406处，示例性方法包括向远程设备(例如，远程设备360)提供干净的语音，以进行进一步处理(诸如，ASR)。
图5示出了可以用于实现本技术的各种实施方式的各种元件(例如，音频设备、控制器等)的示例性计算机系统500。图5的计算机系统500可以在计算系统、网络、服务器或其组合的上下文中实现。图5的计算机系统500包括一个或更多个处理器单元510和主存储器520。主存储器520部分地存储用于由处理器单元510执行的指令和数据。在各种实施方式中，主存储器520在操作时存储可执行代码。图5的计算机系统500还包括一个或更多个海量数据存储设备530、一个或更多个便携式存储设备540、输出设备550、用户输入设备560、图形显示系统570和外围设备580。
图5所示的组件被描绘为经由单个总线590连接。组件可以通过一个或更多个数据传输装置连接。处理器单元510和主存储器520经由本地微处理器总线连接，并且海量数据存储设备530、外围设备580、便携式存储设备540和图形显示系统570经由一个或更多个输入/输出(I/O)总线连接。
可以利用磁盘驱动器、固态驱动器或光盘驱动器实现的海量数据存储设备530是用于存储供处理器单元510使用的数据和指令的非易失性存储设备。海量数据存储设备530存储用于实现本公开的实施方式的系统软件，并且可以在程序执行期间将软件的全部或部分加载到主存储器520中。
便携式存储设备540与便携式非易失性存储介质(诸如，闪存驱动器、软盘、光盘、数字视频盘或通用串行总线(USB)存储设备)协同操作以向图5的计算机系统500输入数据和软件代码以及从图5的计算机系统500输出数据和软件代码。用于实现本公开的实施方式例的系统软件可以被存储在便携式介质上，并经由便携式存储设备540输入到计算机系统500中。
用户输入设备560可以提供用户接口的一部分。用户输入设备560可以包括用于输入和操纵字母数字和其它信息的一个或更多个麦克风、字母数字键盘(诸如，键盘)、指示设备(诸如，鼠标、轨迹球、轨迹板、触笔或光标方向键)。用户输入设备560还可以包括触摸屏。另外，如图5所示的计算机系统500包括输出设备550。合适的输出设备550包括扬声器、打印机、网络接口和监视器。
图形显示系统570包括液晶显示器(LCD)或其它合适的显示设备。图形显示系统570被配置为接收文本和图形信息并处理所述信息以输出到显示设备。
外围设备580可以包括任何类型的计算机支持设备以向计算机系统500添加附加功能。
图5的计算机系统500中设置的组件是通常在计算机系统中找到的那些组件，所述组件可以适用于本公开的实施方式，并且旨在表示本领域公知的这些计算机组件的广泛类别。因此，图5的计算机系统500可以是个人计算机(PC)、手持计算机系统、电话、移动计算机系统、工作站、平板电脑、平板手机、移动电话、服务器、小型计算机、大型计算机、可穿戴设备、嵌入式设备或任何其它计算机系统。计算机还可以包括不同的总线配置、网络平台、多处理器平台等。可以使用各种操作系统，所述各种操作系统包括UNIX、LINUX、WINDOWS、MACOS、PALM OS、QNX ANDROID、IOS、CHROME、TIZEN和其它合适的操作系统。
各种实施方式的处理可以在基于云的软件中实现。在一些实施方式中，计算机系统500被实现为基于云的计算环境(诸如，在计算云内操作的虚拟机)。在其它实施方式中，计算机系统500本身可以包括基于云的计算环境，其中，计算机系统500的功能以分布式的方式执行。因此，当计算机系统500被配置为计算云时，所述计算机系统500可以包括各种形式的多个计算设备，如下面将更详细地描述的。
通常，基于云的计算环境是通常组合一大批处理器(诸如，在web服务器内)的计算能力和/或组合一大批计算机存储器或存储设备的存储容量的资源。提供基于云的资源的系统可以被其所有者专有地利用，或者所述系统对于在计算基础设施内部署应用的其它用户来说可以是可访问的以获得大计算或存储资源的益处。
例如，云可以由包括多个计算设备的网络服务器的网络形成，所述多个计算设备在配置上类似于计算机系统500，每个服务器或至少多个服务器提供处理器和/或存储资源。这些服务器可以管理由多个用户(例如，云资源客户或其它用户)提供的工作负载。通常，每个用户将工作负载需求放置在实时变化的云资源上。这些变化的性质和程度可以取决于例如资源所服务的业务的类型。
以上参照示例实施方式描述了本技术。上面的说明性讨论并不旨在是穷举的或将所公开的主题的实施方式限制为所公开的形式。鉴于上述教导，修改和变化是可能的，以使本领域的其它技术人员能够利用可适合于特定用途的那些实施方式。

Claims (19)

1.一种用于多源噪声抑制的方法，所述方法包括以下步骤：
向音频流分配权重，这些音频流由多个音频设备同步地提供，所述权重取决于所述音频流的质量，其中，分配权重的步骤包括：通过在单个房间和多房间环境中的至少一个中定位、识别和映射目标声音和噪声源来生成声学活动地图，以便创建所述环境的多维声学视图；
基于所述权重对所述音频流执行噪声抑制处理以生成干净的语音信号；
将来自所述噪声抑制处理的所述干净的语音信号提供给至少一个远程设备用于进一步处理；以及
基于所述声学活动地图，选择所述多个音频设备中的最佳的音频设备以与用户通信。
2.根据权利要求1所述的方法，其中，所述多个音频设备中的每一个包括至少一个麦克风，并且所述多个音频设备连接在所连接的设备的动态网络中，使得所述多个音频设备作为物联网环境的一部分被连接。
3.根据权利要求1所述的方法，其中，所述权重与所述音频流的至少一个质量度量成比例，所述质量度量包括至少一个信噪比SNR。
4.根据权利要求1所述的方法，其中，所述方法还包括以下步骤：
执行回波消除。
5.根据权利要求1所述的方法，其中，所述多个音频设备中的一个或更多个被合并到物联网设备中。
6.根据权利要求5所述的方法，所述方法还包括以下步骤：基于位于所述物联网设备附近的一个或更多个声源持续地更新所述多个音频设备之间的声学签名。
7.根据权利要求1所述的方法，其中，使用听觉场景分析和场景分类器来识别目标声音和噪声源。
8.根据权利要求1所述的方法，其中，所述音频流包括时间戳，所述方法还包括以下步骤：基于所述时间戳，将所述多个音频设备同步到公共时间源。
9.根据权利要求8所述的方法，所述方法还包括以下步骤：基于所述声学活动地图，基于SNR质量度量向所述音频流分配权重。
10.根据权利要求9所述的方法，所述方法还包括以下步骤：基于所述声学活动地图，进一步基于提供所述音频流中的相应一个音频流的相关联的音频设备测量噪声的程度，向所述音频流分配权重。
11.根据权利要求1所述的方法，其中，与所述用户的通信是经由所述最佳的音频设备的扬声器。
12.根据权利要求8所述的方法，其中，所述方法还包括以下步骤：
执行回波消除。
13.根据权利要求1所述的方法，其中，所述音频流包括用于执行激活所述远程设备和与另一用户通信中的至少一个的至少一个语音命令。
14.根据权利要求1所述的方法，其中，所述进一步处理包括所述干净的语音信号的自动语音识别ASR处理。
15.根据权利要求14所述的方法，其中，基于所述ASR处理，识别用于连接到另一用户的命令的上下文，并且将所述干净的语音信号传送到所述多个音频设备中的位于所述另一用户附近的至少一个音频设备，以建立与所述另一用户的双向通信。
16.一种用于多源音频处理的系统，所述系统包括：
处理器；以及
存储器，所述存储器与所述处理器在通信上联接，所述存储器存储指令，所述指令当被所述处理器运行时执行方法，所述方法包括以下步骤：
向音频流分配权重，这些音频流由多个音频设备同步地提供，所述权重取决于所述音频流的质量；
基于所述权重对所述音频流执行噪声抑制处理以生成干净的语音信号；以及
将来自所述噪声抑制处理的所述干净的语音信号提供给远程设备用于进一步处理，
其中，所述多个音频设备中的每一个包括至少一个麦克风，并且其中，所述多个音频设备在物理上彼此分开但连接在所连接的设备的动态网络中，使得所述多个音频设备作为物联网环境的一部分被连接。
17.根据权利要求16所述的系统，其中，分配权重包括通过在单个房间和多房间环境中的至少一个中定位、识别和映射目标声音和噪声源来生成声学活动地图，以便创建所述环境的多维声学视图。
18.一种包含指令的非瞬时计算机可读存储介质，所述指令当被至少一个处理器运行时执行方法的步骤，所述方法包括以下步骤：
向音频流分配权重，这些音频流由多个音频设备同步地提供，这些权重取决于所述音频流的质量，其中，分配权重的步骤包括：通过在单个房间和多房间环境中的至少一个中定位、识别和映射目标声音和噪声源来生成声学活动地图，以便创建所述环境的多维声学视图；
基于所述权重对所述音频流执行噪声抑制处理以生成干净的语音信号；
将来自所述噪声抑制处理的所述干净的语音信号提供给至少一个远程设备用于进一步处理；以及
基于所述声学活动地图，选择所述多个音频设备中的最佳的音频设备以与用户通信。
19.根据权利要求18所述的非瞬时计算机可读存储介质，其中，所述多个音频设备中的一个或更多个被合并到物联网设备中。
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