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    • 2. 发明申请
    • WEAPON IDENTIFICATION USING ACOUSTIC SIGNATURES ACROSS VARYING CAPTURE CONDITIONS
    • 使用声音识别的武器识别符合各种不同的捕获条件
    • US20100271905A1
    • 2010-10-28
    • US12766219
    • 2010-04-23
    • Saad KhanAjay DivakaranHarpreet Singh Sawhney
    • Saad KhanAjay DivakaranHarpreet Singh Sawhney
    • G01S3/80
    • G10L25/48
    • A computer implemented method for automatically detecting and classifying acoustic signatures across a set of recording conditions is disclosed. A first acoustic signature is received. The first acoustic signature is projected into a space of a minimal set of exemplars of acoustic signature types derived from a larger set of exemplars using a wrapper method. At least one vector distance is calculated between the projected acoustic signature and each exemplar of the minimal set of exemplars. An exemplar is selected from the minimal set of exemplars having the smallest vector distance to the projected acoustic signature as a class corresponding to and classifying the first acoustic signature. The first acoustic signature and the plurality of acoustic signatures may correspond to one of gunshots, musical instruments, songs, and speech. The minimal set of exemplars may correspond to a hierarchy of acoustic signature types.
    • 公开了一种用于在一组记录条件下自动检测和分类声学签名的计算机实现的方法。 接收到第一个声学签名。 第一声​​学签名被投影到使用包装方法从更大的样本集合导出的声学签名类型的最小样本集合的空间中。 在投影的声学特征与最小样本集的每个样本之间计算至少一个矢量距离。 从具有与投影的声学签名的最小向量距离的最小样本集合中选择一个示例作为对应于和分类第一声学签名的类别。 第一声​​学签名和多个声学签名可以对应于枪声,乐器,歌曲和语音之一。 最小的一组样本可以对应于声学签名类型的层级。
    • 3. 发明授权
    • Weapon identification using acoustic signatures across varying capture conditions
    • 使用声学签名的武器识别在不同的捕获条件下
    • US08385154B2
    • 2013-02-26
    • US12766219
    • 2010-04-23
    • Saad KhanAjay DivakaranHarpreet Singh Sawhney
    • Saad KhanAjay DivakaranHarpreet Singh Sawhney
    • G01S3/80
    • G10L25/48
    • A computer implemented method for automatically detecting and classifying acoustic signatures across a set of recording conditions is disclosed. A first acoustic signature is received. The first acoustic signature is projected into a space of a minimal set of exemplars of acoustic signature types derived from a larger set of exemplars using a wrapper method. At least one vector distance is calculated between the projected acoustic signature and each exemplar of the minimal set of exemplars. An exemplar is selected from the minimal set of exemplars having the smallest vector distance to the projected acoustic signature as a class corresponding to and classifying the first acoustic signature. The first acoustic signature and the plurality of acoustic signatures may correspond to one of gunshots, musical instruments, songs, and speech. The minimal set of exemplars may correspond to a hierarchy of acoustic signature types.
    • 公开了一种用于在一组记录条件下自动检测和分类声学签名的计算机实现的方法。 接收到第一个声学签名。 第一声​​学签名被投影到使用包装方法从更大的样本集合导出的声学签名类型的最小样本集合的空间中。 在投影的声学特征与最小样本集的每个样本之间计算至少一个矢量距离。 从具有与投影的声学签名的最小向量距离的最小样本集合中选择一个范例作为对应于和分类第一声学签名的类别。 第一声​​学签名和多个声学签名可以对应于枪声,乐器,歌曲和语音之一。 最小的一组样本可以对应于声学签名类型的层级。