会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 4. 发明授权
    • System and process for regression-based residual acoustic echo suppression
    • 基于回归的残余声学回声抑制的系统和过程
    • US08416946B2
    • 2013-04-09
    • US12890075
    • 2010-09-24
    • Amit ChhetriArungunram SurendranJack StokesJohn Platt
    • Amit ChhetriArungunram SurendranJack StokesJohn Platt
    • H04M9/08
    • H04M9/082
    • A regression-based residual echo suppression (RES) system and process for suppressing the portion of the microphone signal corresponding to a playback of a speaker audio signal that was not suppressed by an acoustic echo canceller (AEC). In general, a prescribed regression technique is used between a prescribed spectral attribute of multiple past and present, fixed-length, periods (e.g., frames) of the speaker signal and the same spectral attribute of a current period (e.g., frame) of the echo residual in the output of the AEC. This automatically takes into consideration the correlation between the time periods of the speaker signal. The parameters of the regression can be easily tracked using adaptive methods. Multiple applications of RES can be used to produce better results and this system and process can be applied to stereo-RES as well.
    • 基于回归的残差回波抑制(RES)系统和用于抑制对应于未被声学回声消除器(AEC)抑制的扬声器音频信号的重放的麦克风信号的部分的处理。 通常,在多个过去和现在,固定长度的扬声器信号的周期(例如,帧)和当前周期(例如,帧)的相同频谱属性之间使用规定的回归技术 AEC输出中的回波残差。 这自动考虑了扬声器信号的时间段之间的相关性。 可以使用自适应方法轻松跟踪回归的参数。 RES的多个应用可以用于产生更好的结果,并且该系统和过程也可以应用于立体声RES。
    • 5. 发明授权
    • Automatic audio gain control for concurrent capture applications
    • 用于并发捕获应用的自动音频增益控制
    • US08290181B2
    • 2012-10-16
    • US11084608
    • 2005-03-19
    • Jack W. Stokes, IIIJohn PlattDavid Alan Stevens
    • Jack W. Stokes, IIIJohn PlattDavid Alan Stevens
    • H03G3/00H03G9/00
    • H04R3/005H03G3/301H03G3/3089H04R3/02H04R2410/00H04R2430/01
    • A system level automatic gain control (“System AGC”) automatically initializes and controls analog microphone gain in an environment where multiple independent applications simultaneously receive an input from a single analog microphone or microphone array. In one embodiment, the System AGC also prevents those applications from acting to separately control the gain by intercepting external gain control commands and responding to the corresponding application with a corresponding digital gain applied to the input signal from the microphone. Consequently, the System AGC avoids problems relating to oscillations and instability in the microphone gain resulting from multiple applications trying to simultaneously control the gain while preventing each application from adversely affecting the quality of another application's audio capture signal. Further, in one embodiment, the System AGC also acts to maximize the signal to noise (SNR) ratio of the microphone without introducing clipping as a function of a sampled background environment.
    • 在多个独立应用程序同时从单个模拟麦克风或麦克风阵列接收输入的环境中,系统级自动增益控制(系统AGC)自动初始化和控制模拟麦克风增益。 在一个实施例中,系统AGC还防止这些应用通过截取外部增益控制命令来单独地控制增益,并且以对应于来自麦克风的输入信号的相应数字增益作出响应。 因此,系统AGC避免了与多个应用程序尝试同时控制增益相关的麦克风增益的振荡和不稳定性问题,同时防止每个应用程序不利地影响另一应用程序的音频捕获信号的质量。 此外,在一个实施例中,系统AGC还用于使麦克风的信噪比(SNR)最大化,而不会引入作为采样的背景环境的函数的削波。
    • 7. 发明申请
    • Leveraging unlabeled data with a probabilistic graphical model
    • 利用概率图形模型利用未标记的数据
    • US20070005341A1
    • 2007-01-04
    • US11170989
    • 2005-06-30
    • Christopher BurgesJohn Platt
    • Christopher BurgesJohn Platt
    • G06F17/27
    • G06F17/3071
    • A general probabilistic formulation referred to as ‘Conditional Harmonic Mixing’ is provided, in which links between classification nodes are directed, a conditional probability matrix is associated with each link, and where the numbers of classes can vary from node to node. A posterior class probability at each node is updated by minimizing a divergence between its distribution and that predicted by its neighbors. For arbitrary graphs, as long as each unlabeled point is reachable from at least one training point, a solution generally always exists, is unique, and can be found by solving a sparse linear system iteratively. In one aspect, an automated data classification system is provided. The system includes a data set having at least one labeled category node in the data set. A semi-supervised learning component employs directed arcs to determine the label of at least one other unlabeled category node in the data set.
    • 提供了称为“条件谐波混合”的一般概率公式,其中分类节点之间的链接被引导,条件概率矩阵与每个链路相关联,并且类的数量可以在节点之间变化。 通过最小化其分布与其邻居预测的分布之间的差异来更新每个节点处的后级概率。 对于任意图,只要每个未标记的点从至少一个训练点到达,则通常总是存在的解是唯一的,并且可以通过迭代地求解稀疏线性系统来找到。 一方面,提供了一种自动数据分类系统。 该系统包括在数据集中具有至少一个标记类别节点的数据集。 半监督学习组件使用有向弧来确定数据集中至少一个其他未标记类别节点的标签。
    • 8. 发明申请
    • NOISE-ROBUST FEATURE EXTRACTION USING MULTI-LAYER PRINCIPAL COMPONENT ANALYSIS
    • 使用多层主成分分析的噪声强度特征提取
    • US20060217968A1
    • 2006-09-28
    • US11422862
    • 2006-06-07
    • Chris BurgesJohn Platt
    • Chris BurgesJohn Platt
    • G10L19/14
    • G06K9/4647G06K9/6232G10L15/02G10L15/20
    • Extracting features from signals for use in classification, retrieval, or identification of data represented by those signals uses a “Distortion Discriminant Analysis” (DDA) of a set of training signals to define parameters of a signal feature extractor. The signal feature extractor takes signals having one or more dimensions with a temporal or spatial structure, applies an oriented principal component analysis (OPCA) to limited regions of the signal, aggregates the output of multiple OPCAs that are spatially or temporally adjacent, and applies OPCA to the aggregate. The steps of aggregating adjacent OPCA outputs and applying OPCA to the aggregated values are performed one or more times for extracting low-dimensional noise-robust features from signals, including audio signals, images, video data, or any other time or frequency domain signal. Such extracted features are useful for many tasks, including automatic authentication or identification of particular signals, or particular elements within such signals.
    • 从用于分类,检索或识别由这些信号表示的数据的信号中提取特征使用一组训练信号的“失真判别分析”(DDA)来定义信号特征提取器的参数。 信号特征提取器采用具有时间或空间结构的一个或多个维度的信号,将定向主成分分析(OPCA)应用于信号的有限区域,聚合空间或时间相邻的多个OPCA的输出,并应用OPCA 到总计。 执行聚合相邻OPCA输出并将OPCA应用于聚合值的步骤一次或多次,用于从包括音频信号,图像,视频数据或任何其他时间或频域信号的信号中提取低维噪声鲁棒特征。 这些提取的特征对于许多任务是有用的,包括特定信号的自动认证或识别,或这些信号内的特定元件。
    • 9. 发明申请
    • Automatic audio gain control for concurrent capture applications
    • 用于并发捕获应用的自动音频增益控制
    • US20060210096A1
    • 2006-09-21
    • US11084608
    • 2005-03-19
    • Jack StokesJohn PlattDavid Stevens
    • Jack StokesJohn PlattDavid Stevens
    • H03G3/00H03G9/00
    • H04R3/005H03G3/301H03G3/3089H04R3/02H04R2410/00H04R2430/01
    • A system level automatic gain control (“System AGC”) automatically initializes and controls analog microphone gain in an environment where multiple independent applications simultaneously receive an input from a single analog microphone or microphone array. In one embodiment, the System AGC also prevents those applications from acting to separately control the gain by intercepting external gain control commands and responding to the corresponding application with a corresponding digital gain applied to the input signal from the microphone. Consequently, the System AGC avoids problems relating to oscillations and instability in the microphone gain resulting from multiple applications trying to simultaneously control the gain while preventing each application from adversely affecting the quality of another application's audio capture signal. Further, in one embodiment, the System AGC also acts to maximize the signal to noise (SNR) ratio of the microphone without introducing clipping as a function of a sampled background environment.
    • 系统级自动增益控制(“系统AGC”)在多个独立应用程序同时从单个模拟麦克风或麦克风阵列接收输入的环境中自动初始化和控制模拟麦克风增益。 在一个实施例中,系统AGC还防止这些应用通过截取外部增益控制命令来单独地控制增益,并且以对应于来自麦克风的输入信号的相应数字增益作出响应。 因此,系统AGC避免了与多个应用程序尝试同时控制增益相关的麦克风增益的振荡和不稳定性问题,同时防止每个应用程序不利地影响另一应用程序的音频捕获信号的质量。 此外,在一个实施例中,系统AGC还用于使麦克风的信噪比(SNR)最大化,而不会引入作为采样的背景环境的函数的削波。