Abstract: A method and system for secure speaker authentication between a caller device and a first device using an authentication server are provided. The system comprises extracting features into a feature matrix from an incoming audio call; generating a partial i-vector, wherein the partial i-vector includes a first low-order statistic; sending the partial i-vector to the authentication server; and receiving from the authentication server a match score generated based on a full i-vector and another i-vector being stored on the authentication server, wherein the full i-vector is generated from the partial i-vector.

Abstract: A system and method for improving T-matrix training for speaker recognition, comprising receiving an audio input, divisible into a plurality of audio frames including at least an audio sample of a human speaker; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; and generating an optimized T-matrix training sequence computation, based on at least the first i-vector.

Abstract: A system and method for efficient universal background model (UBM) training for speaker recognition, including: receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame of the plurality of audio frames includes an audio sample having a length above a first threshold extracting at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature; generating an optimized training sequence computation based on the feature vector and a Gaussian Mixture Model (GMM), wherein the GMM is associated with a plurality of components, wherein each of the plurality of components is defined by a covariance matrix, a mean vector, and a weight vector; and updating any of the associated components of the GMM based on the generated optimized training sequence computation.

Abstract: A system and method for improving T-matrix training for speaker recognition, comprising receiving an audio input, divisible into a plurality of audio frames including at least an audio sample of a human speaker; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; and generating an optimized T-matrix training sequence computation, based on at least the first i-vector.

Abstract: A method and system for secure speaker authentication between a caller device and a first device using an authentication server are provided. The system comprises extracting features into a feature matrix from an incoming audio call; generating a partial i-vector, wherein the partial i-vector includes a first low-order statistic; sending the partial i-vector to the authentication server; and receiving from the authentication server a match score generated based on a full i-vector and another i-vector being stored on the authentication server, wherein the full i-vector is generated from the partial i-vector.

Abstract: A system and method for enrolling a speaker in a speaker authentication and identification system (AIS), the method comprising: generating a user account, the user account comprising: a user identifier based on one or more metadata elements associated with an audio input received from an end device; generating a first i-vector from an audio frame of the audio input, a trained T-matrix, and a Universal Background Model (UBM), wherein the first i-vector generation comprises an optimized computation; and associating the user account with the first i-vector.

Abstract: A system and method for improving T-matrix training for speaker recognition are provided. The method includes receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame includes an audio sample of a human speaker, the sample having a length above a first threshold; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; generating an optimized T-matrix training sequence computation, based on the first i-vector, an initialized T-matrix, the centered statistics, and a Gaussian mixture model (GMM) of a trained universal background model (UBM).

Abstract: A system and method for efficient universal background model (UBM) training for speaker recognition, including: receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame of the plurality of audio frames includes an audio sample having a length above a first threshold extracting at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature; generating an optimized training sequence computation based on the feature vector and a Gaussian Mixture Model (GMM), wherein the GMM is associated with a plurality of components, wherein each of the plurality of components is defined by a covariance matrix, a mean vector, and a weight vector; and updating any of the associated components of the GMM based on the generated optimized training sequence computation.

Abstract: A system and method for efficient universal background model (UBM) training for speaker recognition, including: receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame of the plurality of audio frames includes an audio sample having a length above a first threshold extracting at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature; generating an optimized training sequence computation based on the feature vector and a Gaussian Mixture Model (GMM), wherein the GMM is associated with a plurality of components, wherein each of the plurality of components is defined by a covariance matrix, a mean vector, and a weight vector; and updating any of the associated components of the GMM based on the generated optimized training sequence computation.

Abstract: A system and method for enrolling a speaker in a speaker authentication and identification system (AIS), the method comprising: generating a user account, the user account comprising: a user identifier based on one or more metadata elements associated with an audio input received from an end device; generating a first i-vector from an audio frame of the audio input, a trained T-matrix, and a Universal Background Model (UBM), wherein the first i-vector generation comprises an optimized computation; and associating the user account with the first i-vector.

Abstract: A system and method for improving T-matrix training for speaker recognition are provided. The method includes receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame includes an audio sample of a human speaker, the sample having a length above a first threshold; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; generating an optimized T-matrix training sequence computation, based on the first i-vector, an initialized T-matrix, the centered statistics, and a Gaussian mixture model (GMM) of a trained universal background model (UBM).

Abstract: A system and method for efficient universal background model (UBM) training for speaker recognition, including: receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame of the plurality of audio frames includes an audio sample having a length above a first threshold extracting at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature; generating an optimized training sequence computation based on the feature vector and a Gaussian Mixture Model (GMM), wherein the GMM is associated with a plurality of components, wherein each of the plurality of components is defined by a covariance matrix, a mean vector, and a weight vector; and updating any of the associated components of the GMM based on the generated optimized training sequence computation.

Abstract: An embodiment of the invention provides a method of creating a statistical model of a baseline capacitance CP of a capacitive sensor located on a capacitive-touch screen. A sensed capacitance CS of a capacitive sensor is measured during a particular state of the electronic device that includes the capacitive-touch screen. When physical contact is not made with the capacitive sensor, the sensed capacitance CS is stored as a baseline capacitance CP. The baseline capacitance CP is then used to create the statistical model for that particular state of the electronic device. When physical contact is made with the capacitive sensor, the value of the baseline capacitance CP of the capacitive sensor is subtracted from the value of the sensed capacitance CS and the result, CF=(CS?CP), is sent to a touch detection circuit.

Abstract: An embodiment of the invention provides a method and apparatus for determining what type of interaction is made with a capacitive touch screen. A capacitance sensor with the largest sensed capacitance in a group of capacitance sensors is determined. Next, a parametric surface is determined from the value of the largest sensed capacitance and the values of the sensed capacitances in the group of capacitance sensors. From the parametric surface, an interpolated peak capacitance, a curvature K at the interpolated peak and an orientation ? at the interpolated peak are determined. Based on the interpolated peak capacitance, the curvature K and the orientation ?, the type of interaction made with the capacitive-touch screen is identified.

Abstract: An embodiment of the invention provides a method of creating a statistical model of a baseline capacitance CP of a capacitive sensor located on a capacitive-touch screen. A sensed capacitance CS of a capacitive sensor is measured during a particular state of the electronic device that includes the capacitive-touch screen. When physical contact is not made with the capacitive sensor, the sensed capacitance CS is stored as a baseline capacitance CP. The baseline capacitance CP is then used to create the statistical model for that particular state of the electronic device. When physical contact is made with the capacitive sensor, the value of the baseline capacitance CP of the capacitive sensor is subtracted from the value of the sensed capacitance CS and the result, CF=(CS?CP), is sent to a touch detection circuit.

Abstract: Systems and methods for identifying a transmission channel response and a feedback channel response from a plurality of composite system responses are disclosed. A plurality of shifted feedback signals are created by shifting a feedback signal frequency by a plurality of first offset values and/or by shifting a transmission signal frequency by a plurality of second offset values. The feedback signals are compared to an input signal to identify the transmission channel response and/or a feedback channel response. A control signal is generated for a pre-distortion circuit to modify the input signal by an inverse of the transmission channel response. The composite system response is measured at a plurality of operating frequencies and at the plurality of offset values. The measurements are stored in a matrix and singular value decomposition is applied to the matrix of measurements to calculate the transmission channel response and feedback channel response.

Abstract: An apparatus is provided. In the apparatus, an input to index (I2I) module maps a complex input into a real signal. A real data tap delay line is coupled to the I2I module and includes N delay-elements. A complex data tap delay line is configured to receive the complex input and includes M delay elements. A set of K of non-linear function modules is also provided. Each non-linear function module from the set has at least one real input, at least one complex input, and at least one complex output. A configurable connectivity crossbar multiplexer couples K of the N real tap delay line elements to real inputs of the set non-linear functions and couples K of the M complex tap delay line elements to complex inputs of the set non-linear function modules.

Abstract: Systems and methods for identifying a transmission channel response and a feedback channel response from a plurality of composite system responses are disclosed. A plurality of shifted feedback signals are created by shifting a feedback signal frequency by a plurality of first offset values and/or by shifting a transmission signal frequency by a plurality of second offset values. The feedback signals are compared to an input signal to identify the transmission channel response and/or a feedback channel response. A control signal is generated for a pre-distortion circuit to modify the input signal by an inverse of the transmission channel response. The composite system response is measured at a plurality of operating frequencies and at the plurality of offset values. The measurements are stored in a matrix and singular value decomposition is applied to the matrix of measurements to calculate the transmission channel response and feedback channel response.

Abstract: Systems and methods for power amplifier pre-distortion are provided. The systems and methods of power amplifier digital pre-distortion disclosed herein may include a generic pre-distorter architecture which can implement a variety of Volterra cross terms involving single dimension convolutions (first order dynamics). For hardware implementations, this generic pre-distorter is further fine-tuned to provide a choice between different sets of cross terms that can be selected for a given PA for optimal performance. The novel pre-distorter architecture provides flexibility to trade off memory depth for additional Volterra terms and vice versa. A further novelty is the ability to trade off both memory depth and cross terms for a higher sample rate operation, which may enable higher order non-linear pre-distortion, or support for higher signal bandwidths. A poly-phase non-linear filtering mode allows for this flexibility.