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    • 1. 发明申请
    • MOBILE DEVICE LOCATION ESTIMATION USING ENVIRONMENTAL INFORMATION
    • 使用环境信息的移动设备位置估计
    • WO2012048027A1
    • 2012-04-12
    • PCT/US2011/054962
    • 2011-10-05
    • QUALCOMM IncorporatedKIM, TaesuYOU, KisunLEE, Te-Won
    • KIM, TaesuYOU, KisunLEE, Te-Won
    • H04L29/08H04W4/02
    • H04W4/043G01S5/0036G01S5/18G06Q30/0261G06Q30/0267H04W4/70
    • Estimating a location of a mobile device is performed by comparing environmental information, such as environmental sound, associated with the mobile device with that of other devices to determine if the environmental information is similar enough to conclude that the mobile device is in a comparable location as another device. The devices may be in comparable locations in that they are in geographically similar locations (e.g., same store, same street, same city, etc.). The devices may be in comparable locations even though they are located in geographically dissimilar locations because the environmental information of the two locations demonstrates that the devices are in the same perceived location. With knowledge that the devices are in comparable locations, and with knowledge of the location of one of the devices, certain actions, such as targeted advertising, may be taken with respect to another device that is within a comparable location.
    • 通过将与移动设备相关联的环境信息与其他设备的环境信息进行比较来确定移动设备的位置是否足够相似,以确定移动设备处于可比较的位置, 另一个设备。 这些设备可以在可比较的位置,因为它们在地理上相似的位置(例如,相同的商店,相同的街道,相同的城市等)。 即使这些设备位于地理位置不同的位置,设备也可处于可比较的位置,因为两个位置的环境信息表明设备处于相同的感知位置。 知道设备处于可比较的位置,并且了解设备之一的位置,可以针对在可比较的位置内的另一设备采取诸如定向广告的某些动作。
    • 4. 发明申请
    • METHOD AND APPARATUS FOR IDENTIFYING MOBILE DEVICES IN SIMILAR SOUND ENVIRONMENT
    • 用于识别类似声环境中移动设备的方法和装置
    • WO2012121855A1
    • 2012-09-13
    • PCT/US2012/025464
    • 2012-02-16
    • QUALCOMM INCORPORATEDKIM, TaesuLEE, Te-Won
    • KIM, TaesuLEE, Te-Won
    • H04L29/08H04W4/04H04W4/08
    • H04L67/18H04W4/023H04W4/043H04W4/08H04W4/21
    • A method for identifying mobile devices in a similar sound environment is disclosed. Each of at least two mobile devices captures an input sound and extracts a sound signature from the input sound. Further, the mobile device extracts a sound feature from the input sound and determines a reliability value based on the sound feature. The reliability value may refer to a probability of a normal sound class given the sound feature. A server receives a packet including the sound signatures and reliability values from the mobile devices. A similarity value between sound signatures from a pair of the mobile devices is determined based on corresponding reliability values from the pair of mobile devices. Specifically, the sound signatures are weighted by the corresponding reliability values. The server identifies mobile devices in a similar sound environment based on the similarity values.
    • 公开了一种用于识别类似声音环境中的移动设备的方法。 至少两个移动设备中的每一个捕获输入声音并从输入声音提取声音签名。 此外,移动设备从输入声音提取声音特征,并且基于声音特征确定可靠性值。 可靠性值可以指给定声音特征的正常声级的概率。 服务器从移动设备接收包括声音签名和可靠性值的数据包。 来自一对移动设备的声音签名之间的相似度值是基于来自该对移动设备的相应的可靠性值来确定的。 具体地说,声音特征被相应的可靠性值加权。 服务器根据相似性值识别类似声音环境中的移动设备。
    • 9. 发明申请
    • SYSTEMS AND METHODS FOR BLIND SOURCE SIGNAL SEPARATION
    • 用于盲源信号分离的系统和方法
    • WO2007100330A1
    • 2007-09-07
    • PCT/US2006/007496
    • 2006-03-01
    • THE REGENTS OF THE UNIVERSITY OF CALIFORNIAKIM, TaesuLEE, Te-Won
    • KIM, TaesuLEE, Te-Won
    • G10L11/00H04B15/00
    • G10L21/0272G10L25/84
    • Signal separation techniques (BSS) based on frequency dependency using a BSS separation process is provided that avoids the substantial permutation problem in signal separation processes (30). The BSS process uses a plurality of signal sources having recognized frequency dependencies and uses these inter-frequency dependencies to robustly separate the source signals. The separation processes receives a set of mixed signal inputs and samples each input using a rolling window process (43). The data is transformed into the frequency domain wherein channel inputs are provided to the inter-frequency dependent separation processes (36). The frequency dependencies are defined for each source and the inter-frequency dependent separation process is able to use the frequency dependency and accurately separate the signals. In one example, the inter-frequency dependent separation process uses a learning algorithm to preserve the frequency dependencies within each source signal and allow for removal of any dependencies between or among the signal sources.
    • 提供了基于使用BSS分离过程的基于频率依赖性的信号分离技术(BSS),其避免信号分离过程中的基本置换问题(30)。 BSS过程使用具有识别的频率依赖性的多个信号源,并且使用这些频率间依赖性来鲁棒地分离源信号。 分离过程接收一组混合信号输入,并使用滚动窗口处理(43)对每个输入进行采样。 数据被转换成频域,其中信道输入被提供给频间相关分离过程(36)。 为每个源定义频率依赖性,并且频率间依赖分离过程能够使用频率依赖性并且精确地分离信号。 在一个示例中,频率间依赖分离过程使用学习算法来保持每个源信号内的频率依赖性,并允许消除信号源之间或之间的任何依赖性。