Abstract: In one example embodiment, speech signals are received from a user during a communication session. The received speech signals contain noise including speech of other individuals. The received speech signals are transformed by a machine learning model to produce transformed speech signals corresponding to the received speech signals with a reduced amount of the noise. The machine learning model is trained with speech of the user satisfying a noise threshold and collected during one or more communication sessions.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio group identification for conferencing. For example, methods may include joining a conference call using a network interface; accessing an audio signal that has been captured using a microphone; detecting a control signal in the audio signal; and, responsive to detection of the control signal, invoking modification of an audio path of the conference call.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for data driven radio enhancement. For example, methods may include demodulating a radio signal to obtain a demodulated audio signal; determining a window of audio samples based on the demodulated audio signal; applying an audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the audio enhancement network includes a machine learning network that has been trained using demodulated audio signals derived from radio signals; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Systems and methods are disclosed for audio enhancement. For example, methods may include accessing audio data; determining a window of audio samples based on the audio data; inputting the window of audio samples to a classifier to obtain a classification, in which the classifier includes a neural network and the classification takes a value from a set of multiple classes of audio; selecting, based on the classification, an audio enhancement network from a set of multiple audio enhancement networks; applying the selected audio enhancement network to the window of audio samples to obtain an enhanced audio segment, in which the selected audio enhancement network includes a neural network that has been trained using audio signals of a type associated with the classification; and storing, playing, or transmitting an enhanced audio signal based on the enhanced audio segment.

Abstract: Methods and apparatus are provided for simultaneous estimation of frequency offset and channel response for a communication system, such as a MU-MIMO communication system. An iterative method is provided for estimating frequency offset and channel response for a plurality of frequency resources. The channel response is estimated for a set of users sharing a given one of the frequency resources. In addition, the frequency offset is estimated for the users in the set, wherein the channel response and frequency offset of users not in the set are maintained at their latest updated values. Initially, the channel response of a user can be an ideal channel response and the frequency offset can be approximately zero.

Abstract: A digital signal processor and method are disclosed with one or more non-linear functions using factorized polynomial interpolation. A digital signal processor evaluates a non-linear function for a value, x, by obtaining two or more values from at least one look-up table for said non-linear function that are near said value, x; and interpolating said two or more obtained values to obtain a value, y, using a factorized polynomial interpolation.

Abstract: A digital signal processor is provided having an instruction set with an xK function that uses a reduced look-up table. The disclosed digital signal processor evaluates an xK function for an input value, x, by computing Log(x) in hardware; multiplying the Log(x) value by K; and determining the xK function by applying an exponential function in hardware to a result of the multiplying step. One or more of the computation of Log(x) and the exponential function employ at least one look-up table having entries with a fewer number of bits than a number of bits in the input value, x.

Abstract: Methods and apparatus are provided for a digital signal processor having an instruction set with one or more non-linear complex functions. A method is provided for a processor. One or more non-linear complex software instructions are obtained from a program. The non-linear complex software instructions have at least one complex number as an input. One or more non-linear complex functions are applied from a predefined instruction set to the at least one complex number. An output is generated comprised of one complex number or two real numbers. A functional unit can implement the one or more non-linear complex functions. In one embodiment, a vector-based digital signal processor is disclosed that processes a complex vector comprised of a plurality of complex numbers. The processor can process the plurality of complex numbers in parallel.

Abstract: A digital signal processor is provided having an instruction set with a logarithm function that uses a reduced look-up table.

Abstract: A digital signal processor is provided having an instruction set with an exponential function that uses a reduced look-up table. The disclosed digital signal processor evaluates an exponential function for an input value, x, by decomposing the input value, x, to an integer part, N, a first fractional part, q1, larger than a specified value, x0, and a second fractional part, q2, smaller than the specified value, x0; computing 2q2 using a polynomial approximation, such as a cubic approximation; obtaining 2q1 from a look-up table; and evaluating the exponential function for the input value, x, by multiplying 2q2, 2q1 and 2N together. Look-up table entries have a fewer number of bits than a number of bits in the input value, x.

Abstract: A method and system for canonical channel estimation in the Long Term Evolution uplink where a multi-frequency signal is generated and then converted to frequency spectrum which is then convolved in the frequency domain with a truncated window function to obtain a time domain channel impulse response. The time domain channel impulse response can be then transformed to a frequency domain to produce a down sampled user channel response, which can be then linearly interpolated to provide a channel estimate for a plurality of subcarriers. Such an approach achieves channel estimation within Long Term Evolution at only canonical locations to reduce complexity without loss in channel entropy.

Abstract: A digital signal processor and method are disclosed having an instruction set with one or more non-linear functions using a look-up table of reduced size. A digital signal processor evaluates a non-linear function for a value, x, by obtaining two or more values for the non-linear function that are near the value, x, from at least one look-up table, wherein the at least one look-up table stores a subset of values for the non-linear function; and interpolating the two or more obtained values to obtain a result, y. The interpolation may comprise, for example, a linear interpolation or a polynomial interpolation. In a further variation, a modulo arithmetic operation can be employed for a periodic non-linear function.