公开(公告)号：US06879952B2

公开(公告)日：2005-04-12

申请号：US09842416

申请日：2001-04-25

申请人： Alejandro Acero ,  Steven J. Altschuler ,  Lani Fang Wu

发明人： Alejandro Acero ,  Steven J. Altschuler ,  Lani Fang Wu

IPC分类号： G10L11/02 ,  G10L21/02 ,  G10L19/12

CPC分类号： G10L25/78 ,  G10L21/0264 ,  G10L2021/02082 ,  G10L2021/02161

摘要： Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.

摘要翻译： 公开了基于目标声源的先验知识的声源分离，不排列，使用卷积混合独立分量分析。 目标声源可以是人的扬声器。 在声源分离中使用的重建滤波器考虑了目标声源的先验知识，例如估计目标声源的频谱。 滤波器通常可以基于语音识别系统来构造。 将语音识别系统的词典与重构的信号进行匹配，表示是否发生了适当的分离。 更具体地说，滤波器可以基于表示典型声源模式的矢量的矢量量化码本构成。 将码本的向量与重构信号进行匹配，表示是否发生了适当的分离。 矢量可以是线性预测矢量等等。

公开(公告)号：US07047189B2

公开(公告)日：2006-05-16

申请号：US10992051

申请日：2004-11-18

申请人： Alejandro Acero ,  Steven J. Altschuler ,  Lani Fang Wu

发明人： Alejandro Acero ,  Steven J. Altschuler ,  Lani Fang Wu

IPC分类号： G10L19/12

CPC分类号： G10L25/78 ,  G10L21/0264 ,  G10L2021/02082 ,  G10L2021/02161

摘要： Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.

摘要翻译： 公开了基于目标声源的先验知识的声源分离，不排列，使用卷积混合独立分量分析。 目标声源可以是人的扬声器。 在声源分离中使用的重建滤波器考虑了目标声源的先验知识，例如估计目标声源的频谱。 滤波器通常可以基于语音识别系统来构造。 将语音识别系统的词典与重构的信号进行匹配，表示是否发生了适当的分离。 更具体地说，滤波器可以基于表示典型声源模式的矢量的矢量量化码本构成。 将码本的向量与重构信号进行匹配，表示是否发生了适当的分离。 矢量可以是线性预测矢量等等。