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    • 77. 发明公开
    • PROXIMITY DISCOVERY USING AUDIO SIGNALS
    • 接近发现使用音频信号
    • EP3161508A1
    • 2017-05-03
    • EP15739067.5
    • 2015-06-22
    • Microsoft Technology Licensing, LLC
    • SHAYANDEH, ShahinICKMAN, StevenPORTNOY, William
    • G01S5/18
    • H04R29/00G01S5/0252G01S5/18
    • Various technologies pertaining to computing data that is indicative of a location of a client computing device are described herein. A client computing device is configured to capture an audio signal, the audio signature being indicative of acoustics of surroundings of the client computing device. A signature is generated based upon a high frequency portion of the captured audio signal, and the signature is compared with other signatures. The other signatures are generated based upon high frequency portions of audio signals captured by other computing devices. A determination regarding the client computing device being co-located with a second client computing device is made based upon the comparison of the signature with the other signatures.
    • 这里描述了关于计算表示客户端计算设备的位置的数据的各种技术。 客户端计算设备被配置为捕获音频信号,音频签名指示客户端计算设备的周围环境的声学。 基于捕获的音频信号的高频部分生成签名,并将签名与其他签名进行比较。 其他签名是基于由其他计算设备捕获的音频信号的高频部分生成的。 基于签名与其他签名的比较来做出关于客户端计算设备与第二客户端计算设备位于同一地点的确定。
    • 79. 发明公开
    • LEARNING MULTIMEDIA SEMANTICS FROM LARGE-SCALE UNSTRUCTURED DATA
    • LERNEN VON MULTIMEDIA-SEMANTIK AUS UMFASSENDEN UNSTRUKTURIERTERN DATEN
    • EP3138051A1
    • 2017-03-08
    • EP15721467.7
    • 2015-04-24
    • Microsoft Technology Licensing, LLC
    • HUA, Xian-ShengLI, JinUSHIKU, Yoshitaka
    • G06N99/00
    • G06F17/30705G06F17/30675G06F17/30864G06N99/005
    • Systems and methods for learning topic models from unstructured data and applying the learned topic models to recognize semantics for new data items are described herein. In at least one embodiment, a corpus of multimedia data items associated with a set of labels may be processed to generate a refined corpus of multimedia data items associated with the set of labels. Such processing may include arranging the multimedia data items in clusters based on similarities of extracted multimedia features and generating intra-cluster and inter-cluster features. The intra-cluster and the inter-cluster features may be used for removing multimedia data items from the corpus to generate the refined corpus. The refined corpus may be used for training topic models for identifying labels. The resulting models may be stored and subsequently used for identifying semantics of a multimedia data item input by a user.
    • 本文描述了用于从非结构化数据学习主题模型并应用所学习的主题模型以识别新数据项的语义的系统和方法。 在至少一个实施例中,可以处理与一组标签相关联的多媒体数据项的语料库以产生与该组标签相关联的多媒体数据项的精简语料库。 这种处理可以包括基于所提取的多媒体特征的相似性来排列多媒体数据项,并且生成集群内和集群间特征。 集群内和集群间特征可以用于从语料库中移除多媒体数据项以产生精炼的语料库。 精致的语料库可用于培训用于识别标签的主题模型。 所得到的模型可以被存储并随后用于识别由用户输入的多媒体数据项的语义。