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    Systems and methods that detect a desired signal via a linear discriminative classifier that utilizes an estimated posterior signal-to-noise ratio (SNR)
    4.
    发明申请
    Systems and methods that detect a desired signal via a linear discriminative classifier that utilizes an estimated posterior signal-to-noise ratio (SNR) 有权
    通过利用估计的后验信噪比（SNR）的线性鉴别分类器来检测期望信号的系统和方法，

    公开(公告)号：US20050091050A1

    公开(公告)日：2005-04-28

    申请号：US10795618

    申请日：2004-03-08

    申请人： Arungunram Surendran ,  John Platt ,  Somsak Sukittanon ,  Christopher Burges

    发明人： Arungunram Surendran ,  John Platt ,  Somsak Sukittanon ,  Christopher Burges

    IPC分类号： G06K9/00 ,  G10L11/02 ,  G10L21/00

    CPC分类号： G06K9/00536 ,  G10L25/78

    摘要： The present invention provides systems and methods for signal detection and enhancement. The systems and methods utilize one or more discriminative classifiers (e.g., a logistic regression model and a convolutional neural network) to estimate a posterior probability that indicates whether a desired signal is present in a received signal. The discriminative estimators generate the estimated probability based on one or more signal-to-noise ratio (SNRs) (e.g., a normalized logarithmic posterior SNR (nlpSNR) and a mel-transformed nlpSNR (mel-nlpSNR)) and an estimated noise model. Depending on the resolution desired, the estimated SNR can be generated at a frame level or at an atom level, wherein the atom level estimates are utilized to generate the frame level estimate. The novel systems and methods can be utilized to facilitate speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation.

    摘要翻译： 本发明提供了用于信号检测和增强的系统和方法。 系统和方法利用一个或多个鉴别分类器（例如，逻辑回归模型和卷积神经网络）来估计指示所接收信号中是否存在期望信号的后验概率。 鉴别估计器基于一个或多个信噪比（SNR）（例如，归一化对数后验SNR（nlpSNR）和mel变换的nlpSNR（mel-nlpSNR））和估计的噪声模型来生成估计概率。 根据期望的分辨率，估计的SNR可以在帧级或原子级产生，其中原子级估计用于生成帧级估计。 可以利用新颖的系统和方法来促进语音检测，语音识别，语音编码，噪声适应，语音增强，麦克风阵列和回声消除。

    Systems and methods that detect a desired signal via a linear discriminative classifier that utilizes an estimated posterior signal-to-noise ratio (SNR)
    5.
    发明授权
    Systems and methods that detect a desired signal via a linear discriminative classifier that utilizes an estimated posterior signal-to-noise ratio (SNR) 有权
    通过利用估计的后验信噪比（SNR）的线性鉴别分类器来检测期望信号的系统和方法，

    公开(公告)号：US07660713B2

    公开(公告)日：2010-02-09

    申请号：US10795618

    申请日：2004-03-08

    申请人： Arungunram C. Surendran ,  John C. Platt ,  Somsak Sukittanon ,  Christopher J. C. Burges

    发明人： Arungunram C. Surendran ,  John C. Platt ,  Somsak Sukittanon ,  Christopher J. C. Burges

    IPC分类号： G10L21/00

    CPC分类号： G06K9/00536 ,  G10L25/78

    摘要： The present invention provides systems and methods for signal detection and enhancement. The systems and methods utilize one or more discriminative classifiers (e.g., a logistic regression model and a convolutional neural network) to estimate a posterior probability that indicates whether a desired signal is present in a received signal. The discriminative estimators generate the estimated probability based on one or more signal-to-noise ratio (SNRs) (e.g., a normalized logarithmic posterior SNR (nlpSNR) and a mel-transformed nlpSNR (mel-nlpSNR)) and an estimated noise model. Depending on the resolution desired, the estimated SNR can be generated at a frame level or at an atom level, wherein the atom level estimates are utilized to generate the frame level estimate. The novel systems and methods can be utilized to facilitate speech detection, speech recognition, speech coding, noise adaptation, speech enhancement, microphone arrays and echo-cancellation.

    摘要翻译： 本发明提供了用于信号检测和增强的系统和方法。 系统和方法利用一个或多个鉴别分类器（例如，逻辑回归模型和卷积神经网络）来估计指示所接收信号中是否存在期望信号的后验概率。 鉴别估计器基于一个或多个信噪比（SNR）（例如，归一化对数后验SNR（nlpSNR）和mel变换的nlpSNR（mel-nlpSNR））和估计的噪声模型来生成估计概率。 根据期望的分辨率，估计的SNR可以在帧级或原子级产生，其中原子级估计用于生成帧级估计。 可以利用新颖的系统和方法来促进语音检测，语音识别，语音编码，噪声适应，语音增强，麦克风阵列和回声消除。