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    • 5. 发明授权
    • Method and apparatus for noise filtering
    • 用于噪声滤波的方法和装置
    • US07110944B2
    • 2006-09-19
    • US11191105
    • 2005-07-27
    • Radu Victor BalanJustinian Rosca
    • Radu Victor BalanJustinian Rosca
    • G10L21/02
    • G10L21/0208H04R3/005
    • A method of filtering noise from a mixed sound signal to obtain a filtered target signal, includes inputting the mixed signal through a plurality of sensors into a plurality of channels, separately Fourier transforming each the mixed signal into the frequency domain, computing a signal short-time spectral amplitude |Ŝ| from the transformed signals, computing a signal short-time spectral complex exponential ei arg(S) from said transformed signals, where arg(S) is the phase of the target signal in the frequency domain, computing said target signal S in the frequency domain from said spectral amplitude and said complex exponential, and computing a spectral power matrix and using the spectral power matrix to compute the spectral amplitude and the spectral complex exponential.
    • 一种从混合声音信号中滤除噪声以获得滤波的目标信号的方法,包括通过多个传感器将混合信号输入多个信道,将混合信号中的每一个分别进行傅里叶变换到频域, 时间谱振幅| S | 从变换的信号中,从所述变换的信号计算信号短时频谱复指数e(S),其中arg(S)是频域中的目标信号的相位,计算 从所述频谱幅度和所述复指数的频域中的所述目标信号S,并计算频谱功率矩阵并使用频谱功率矩阵来计算频谱幅度和频谱复数指数。
    • 6. 发明授权
    • Optimal ratio estimator for multisensor systems
    • 多传感器系统的最优比率估计器
    • US06868365B2
    • 2005-03-15
    • US10435206
    • 2003-05-09
    • Radu Victor BalanJustinian Rosca
    • Radu Victor BalanJustinian Rosca
    • G06K9/00G06K9/62G06F15/17
    • G06K9/6245
    • A signal processing technique can be effectively used for source separation, signal enhancement, and noise reduction when using a twin microphone system. The class of stochastic signals for which ratio-estimates can be computed from histograms is defined. This class fits real-world signals of interest such as voice signals. Theoretical computation in closed form of the optimal estimator for this class of signals is disclosed. Two practical implementation solutions are disclosed, as is a practical solution to exploit an echoic environment model. Furthermore, two novel techniques for signal demixing are presented. The application of the optimal estimator and the suboptimal estimator to the case of more than two channels is disclosed.
    • 当使用双麦克风系统时,信号处理技术可以有效地用于源分离,信号增强和降噪。 定义可以从直方图计算比率估计的随机信号类。 这个类适合现实世界的信号,如语音信号。 公开了这类信号的最优估计器的封闭形式的理论计算。 公开了两个实际的实现解决方案,作为利用回波环境模型的实际解决方案。 此外,提出了两种用于信号分类的新颖技术。 公布了最佳估计器和次优估计器在多于两个通道情况下的应用。
    • 7. 发明授权
    • Method for congestion detection in packet transmission networks
    • 分组传输网络拥塞检测方法
    • US07916658B2
    • 2011-03-29
    • US12206069
    • 2008-09-08
    • Radu Victor BalanChih-Wei HuangJustinian RoscaOctavian Sarca
    • Radu Victor BalanChih-Wei HuangJustinian RoscaOctavian Sarca
    • G06F11/30H04L12/56
    • H04L47/10H04L43/00H04L43/0847H04L43/0888H04L43/0894H04L47/11H04L47/14H04L47/28H04W28/10
    • A method for measuring degree of packet congestion on a channel of a packet communication network. The method includes: during a training mode, generating an mathematical relationship between the degree of packet congestion on the channel and a plurality of measurable features of the network over a plurality of network conditions; and, during a subsequent normal operating mode, periodically measuring the plurality of measurable features and applying the generated mathematical relationship to such periodically measured plurality of measurable features to determine actual degree of congestion on the channel; and comparing the actual degree of congestion on the channel with a predetermined channel congestion threshold level. The degree of packet congestion on the channel is saturation level. The measurable features include: time delay between transmission starts and terminations of the previously transmitted packet; the fraction of time the channel is busy; and, average number of packet transmission retries.
    • 一种用于测量分组通信网络的信道上的分组拥塞程度的方法。 该方法包括:在训练模式期间,在多个网络条件下,在信道上的分组拥塞程度与网络的多个可测量特征之间产生数学关系; 并且在随后的正常操作模式期间,周期性地测量所述多个可测量特征并将所生成的数学关系应用于所述周期性测量的多个可测量特征以确定所述信道上的实际拥塞程度; 以及将所述信道上的实际拥塞程度与预定信道拥塞阈值级别进行比较。 信道上的分组拥塞程度是饱和度。 可测量的特征包括:传输开始之间的时间延迟和先前发送的分组的终止; 频道繁忙时间的一小部分; 并且平均分组传输重试次数。
    • 9. 发明申请
    • Method for Congestion Detection in Packet Transmission Networks
    • 分组传输网络拥塞检测方法
    • US20090141650A1
    • 2009-06-04
    • US12206069
    • 2008-09-08
    • Radu Victor BalanChih-Wei HuangJustinian RoscaOctavian Sarca
    • Radu Victor BalanChih-Wei HuangJustinian RoscaOctavian Sarca
    • G06F11/30
    • H04L47/10H04L43/00H04L43/0847H04L43/0888H04L43/0894H04L47/11H04L47/14H04L47/28H04W28/10
    • A method for measuring degree of packet congestion on a channel of a packet communication network. The method includes: during a training mode, generating an mathematical relationship between the degree of packet congestion on the channel and a plurality of measurable features of the network over a plurality of network conditions; and, during a subsequent normal operating mode, periodically measuring the plurality of measurable features and applying the generated mathematical relationship to such periodically measured plurality of measurable features to determine actual degree of congestion on the channel; and comparing the actual degree of congestion on the channel with a predetermined channel congestion threshold level. The degree of packet congestion on the channel is saturation level. The measurable features include: time delay between transmission starts and terminations of the previously transmitted packet; the fraction of time the channel is busy; and, average number of packet transmission retries.
    • 一种用于测量分组通信网络的信道上的分组拥塞程度的方法。 该方法包括:在训练模式期间,在多个网络条件下,在信道上的分组拥塞程度与网络的多个可测量特征之间产生数学关系; 并且在随后的正常操作模式期间,周期性地测量所述多个可测量特征并将所生成的数学关系应用于所述周期性测量的多个可测量特征以确定所述信道上的实际拥塞程度; 以及将所述信道上的实际拥塞程度与预定信道拥塞阈值级别进行比较。 信道上的分组拥塞程度是饱和度。 可测量的特征包括:传输开始之间的时间延迟和先前发送的分组的终止; 频道繁忙时间的一小部分; 并且平均分组传输重试次数。
    • 10. 发明授权
    • Multichannel voice detection in adverse environments
    • 不利环境中的多声道语音检测
    • US07146315B2
    • 2006-12-05
    • US10231613
    • 2002-08-30
    • Radu Victor BalanJustinian RoscaChristophe Beaugeant
    • Radu Victor BalanJustinian RoscaChristophe Beaugeant
    • G10L15/20
    • G10L25/78G10L2021/02165
    • A multichannel source activity detection system, e.g., a voice activity detection (VAD) system, and method that exploits spatial localization of a target audio source is provided. The method includes the steps of receiving a mixed sound signal by at least two microphones; Fast Fourier transforming each received mixed sound signal into the frequency domain; filtering the transformed signals to output a signal corresponding to a spatial signature of a source; summing an absolute value squared of the filtered signal over a predetermined range of frequencies; and comparing the sum to a threshold to determine if a voice is present. Additionally, the filtering step includes multiplying the transformed signals by an inverse of a noise spectral power matrix, a vector of channel transfer function ratios, and a source signal spectral power.
    • 提供了多通道源活动检测系统,例如语音活动检测(VAD)系统和利用目标音频源的空间定位的方法。 该方法包括通过至少两个麦克风接收混合声音信号的步骤; 将每个接收的混合声音信号快速傅里叶变换到频域; 对经变换的信号进行滤波以输出与源的空间签名相对应的信号; 在预定的频率范围内对滤波信号的绝对值进行求和; 以及将所述和与阈值进行比较以确定是否存在语音。 另外,滤波步骤包括将经变换的信号乘以噪声频谱功率矩阵的倒数,信道传递函数比的向量和源信号频谱功率。