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1. 发明申请

WO2013030134A1 METHOD AND APPARATUS FOR ACOUSTIC SOURCE SEPARATION 审中-公开
标题翻译：声源分离的方法和装置
公开(公告)号：WO2013030134A1
公开(公告)日：2013-03-07
申请号：PCT/EP2012/066549
申请日：2012-08-24
申请人： THE QUEEN'S UNIVERSITY OF BELFAST , MING, Ji , SRINIVASAN, Ramji , CROOKES, Daniel
发明人： MING, Ji , SRINIVASAN, Ramji , CROOKES, Daniel
IPC分类号： G10L21/02
CPC分类号： G10L21/0208 , G10L2021/02087
摘要： A method of separating a plurality of source audio signals from a mixed signal. The method comprises: selecting respective training data for each source audio signal; for respective segments of data representing the mixed signal, determining which combination of a respective segment from each of the selected training data matches the respective segment of the data representing said mixed signal; and reconstructing each source audio signal using the segments of the respective selected training data that form a respective matching combination for each segment of said data representing the mixed signal. The method facilitates separating component speech signals from a single channel mixture of multiple speech signals.
摘要翻译：一种从混合信号中分离多个源音频信号的方法。该方法包括：为每个源音频信号选择相应的训练数据; 对于表示混合信号的各个数据段，确定来自所选择的训练数据中的每一个的相应段的组合与表示所述混合信号的数据的相应段匹配; 以及使用形成表示混合信号的所述数据的每个段的相应匹配组合的各个选择的训练数据的段来重构每个源音频信号。该方法有助于从多个语音信号的单个信道混合分离分量语音信号。

2. 发明申请

WO2005045805A1 VECTOR CLASSIFICATION 审中-公开
标题翻译：矢量分类
公开(公告)号：WO2005045805A1
公开(公告)日：2005-05-19
申请号：PCT/GB2004/004099
申请日：2004-09-27
申请人： THE QUEEN'S UNIVERSITY OF BELFAST , MING, Ji
发明人： MING, Ji
IPC分类号： G10L15/20
CPC分类号： G10L15/20 , G10L15/063 , G10L21/0216
摘要： A method of vector classification is disclosed where a test vector is compared with a set of model vectors. Each model vector corresponds to an ideal vector corrupted by different levels of flat spectrum noise. A full band corruption is effectively converted to a series of partial corruptions, and a score is calculated for each model vector based on the matched components. The most severely mismatched components are ignored. The vector classification can take the form of a speech recognition system, or an image recogniser.
摘要翻译：公开了一种矢量分类的方法，其中测试矢量与一组模型矢量进行比较。每个模型向量对应于由不同级别的平面频谱噪声损坏的理想矢量。全波段损坏被有效地转换为一系列部分损坏，并且基于匹配的分量计算每个模型向量的分数。最严重不匹配的组件被忽略。矢量分类可以采用语音识别系统或图像识别器的形式。

3. 发明申请

WO2004036546A1 CLASSIFICATION OF VECTORS IN NOISY CONDITIONS 审中-公开
标题翻译：噪声条件下的矢量分类
公开(公告)号：WO2004036546A1
公开(公告)日：2004-04-29
申请号：PCT/GB2003/004111
申请日：2003-09-15
申请人： THE QUEEN'S UNIVERSITY OF BELFAST , MING, Ji
发明人： MING, Ji
IPC分类号： G10L15/20
CPC分类号： G06K9/6228 , G10L15/142 , G10L15/20
摘要： A method for vector classification employs a statistical method, the posterior union model, for signal processing and pattern classification in noisy conditions, requiring no knowledge about the noise characteristics. According to the method, avector X to be classified comprises N components, M of which are corrupt, and belongs to one of Q classes C 1 , C 2 , ..., C Q , and where X N-M denotes the subset of the (N-M) clean components in vector X, the method comprising the step of; performing a maximum a posteriori (MAP) probability decision to determine the class C i to which the vector X is deemed to belong; wherein the largest modulus value of P (C i | X N-M ) , is determined for specific values of M and for specific C i ' s, and wherein P (X N-M | C i ) is calculated by performing a disjunction over all possible subsets of (N-M) components taken from X.
摘要翻译：用于矢量分类的方法采用统计方法，后联合模型，用于噪声条件下的信号处理和模式分类，不需要关于噪声特性的知识。根据该方法，待分类的矢量X包括N个分量，其中M个被破坏，并且属于Q个类C1，C2，...，CQ中的一个，并且其中，XN-M表示（NM）在向量X中的清洁组件，该方法包括以下步骤：执行最大后验（MAP）概率决定来确定矢量X被认为属于的类Ci; 其中对于M的特定值和对于特定Ci的确定P（Ci || XN-M）的最大模数值，并且其中P（XN-M || Ci）是通过执行所有可能的分离来计算的（NM）组分的子集取自X.

4. 发明申请

WO2002095730A1 INTERPRETATION OF FEATURES FOR SIGNAL PROCESSING AND PATTERN RECOGNITION 审中-公开
标题翻译：信号处理和模式识别特征的解释
公开(公告)号：WO2002095730A1
公开(公告)日：2002-11-28
申请号：PCT/GB2002/002197
申请日：2002-05-20
申请人： QUEEN'S UNIVERSITY OF BELFAST , MING, Ji
发明人： MING, Ji
IPC分类号： G10L15/20
CPC分类号： G10L15/20 , G10L15/142 , G10L19/0204 , G10L21/0232
摘要： A method of interpretation of features for signal processing and pattern recognition provides a model in which the pattern or signal to be interpreted is considered as a set of N observations, M of which are corrupt, and a disjunction is performed over all possible combinations of N different values (1,...,N) taken N-M at a time. The value of M defines the order of the model, and is determined using an optimality criterion which chooses the order that corresponds to a clean signal based on comparing the state duration probability of the signal or pattern to be interpreted with that of a clean signal.
摘要翻译：用于信号处理和模式识别的特征的解释的方法提供了一种模型，其中要解释的模式或信号被认为是一组N个观察值，其中M个被破坏，并且对所有可能的组合N进行分离不同的值（1，...，N）一次取NM。 M的值定义了模型的顺序，并且使用优化准则来确定，该最优性准则通过将待解释的信号或模式的状态持续时间概率与干净信号的状态持续时间概率进行比较来选择对应于干净信号的顺序。

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