Abstract: An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.

Abstract: Systems, methods, and devices detect audio signals. Methods may include receiving an audio input at an audio front end circuit, and identifying, using a first circuit, a wake word based, at least in part, on the received audio signal. Methods may also include determining if the identifying performed by the first circuit should be modified based, at least in part, on a designated threshold of accuracy associated with the first circuit and a result of the identifying, the designated threshold of accuracy being determined based, at least in part, on a language processing model of a second circuit. Moreover, the modifying may include adjusting wake word detection operations of the first circuit based, at least in part, on a result of the determining.

Abstract: Systems, methods, and devices detect wake signals included in audio signals. Methods include receiving a dataset including raw audio data, the raw audio data comprising a plurality of audio samples and associated metadata, and generating, using one or more processing elements, an augmented dataset based on the raw audio data, the augmented dataset comprising a plurality of annotations identifying types of raw audio data. Methods further include generating, using the one or more processing elements, a feature dataset by extracting features from the augmented dataset based, at least in part, on the plurality of annotations, and generating, using the one or more processing elements, a wake signal detection model based, at least in part, on the feature dataset, the wake signal detection model being a machine learning model trained based on the feature dataset.

Abstract: An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.

Abstract: Described are techniques for noise-robust and speaker-independent keyword spotting (KWS) in an input audio signal that contains keywords used to activate voice-based human-computer interactions. A KWS system may combine the latent representation generated by a denoising autoencoder (DAE) with audio features extracted from the audio signal using a machine learning approach. The DAE may be a discriminative DAE trained with a quadruplet loss metric learning approach to create a highly-separable latent representation of the audio signal in the audio input feature space. In one aspect, spectral characteristics of the audio signal such as Log-Mel features are combined with the latent representation generated by a quadruplet loss variational DAE (QVDQE) as input to a DNN KWS classifier.

Abstract: Implementations disclosed describe methods and systems to perform the methods of optimizing a size of memory used for accumulation of neural node outputs and for supporting multiple computational paths in neural networks. In one example, a size of memory used to perform neural layer computations is reduced by performing nodal computations in multiple batches, followed by rescaling and accumulation of nodal outputs. In another example, execution of parallel branches of neural node computations include evaluating, prior to the actual execution, the amount of memory resources needed to execute a particular order of branches sequentially and select the order that minimizes this amount or keeps this amount below a target threshold.

Abstract: Implementations disclosed describe methods and systems to perform the methods of optimizing a size of memory used for accumulation of neural node outputs and for supporting multiple computational paths in neural networks. In one example, a size of memory used to perform neural layer computations is reduced by performing nodal computations in multiple batches, followed by rescaling and accumulation of nodal outputs. In another example, execution of parallel branches of neural node computations include evaluating, prior to the actual execution, the amount of memory resources needed to execute a particular order of branches sequentially and select the order that minimizes this amount or keeps this amount below a target threshold.

Abstract: An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.

Abstract: An example method of estimating the angular resolution of antenna array comprises: receiving a plurality of values of magnitude and phase of a radio frequency (RF) signal for each antenna element of a plurality of antenna elements comprised by an antenna array; performing, by a machine learning model, a feature extraction operation to transform the plurality of values of magnitude and phase into a plurality of data points in a reduced-dimension space; clustering, by the machine learning model, the plurality of data points into a plurality of clusters; and computing, based on the clustered data points, an angular resolution value for the antenna array.

Abstract: An example method of estimating the angular resolution of antenna array comprises: receiving a plurality of values of magnitude and phase of a radio frequency (RF) signal for each antenna element of a plurality of antenna elements comprised by an antenna array; performing, by a machine learning model, a feature extraction operation to transform the plurality of values of magnitude and phase into a plurality of data points in a reduced-dimension space; clustering, by the machine learning model, the plurality of data points into a plurality of clusters; and computing, based on the clustered data points, an angular resolution value for the antenna array.

Abstract: An example method of estimating the angular resolution of antenna array comprises: receiving a plurality of values of magnitude and phase of a radio frequency (RF) signal for each antenna element of a plurality of antenna elements comprised by an antenna array; performing, by a machine learning model, a feature extraction operation to transform the plurality of values of magnitude and phase into a plurality of data points in a reduced-dimension space; clustering, by the machine learning model, the plurality of data points into a plurality of clusters; and computing, based on the clustered data points, an angular resolution value for the antenna array.

Abstract: An example method for estimating the angle-of-arrival (AoA) and other parameters of radio frequency (RF) signals that are received by an antenna array comprises: receiving a plurality of radio frequency (RF) signal power measurements by a plurality of antenna elements at a plurality of RF channels; computing, by applying a machine learning model to the plurality of RF signal power measurements, an estimated RF signal parameter value; and outputting the RF signal parameter value.

Abstract: A system includes a transceiver configured to receive frequency dependent channel estimates or beamforming feedback in a multi-carrier, multi-antenna communication system, and a multi-layer perceptron feed forward neural network component, coupled with the transceiver, configured to estimate parameters of multipath reflections using representations of the channel estimates or beamforming feedback, and to generate transmission correction factors for the transceiver.

Abstract: A system includes a transceiver configured to receive frequency dependent channel estimates or beamforming feedback in a multi-carrier, multi-antenna communication system, and a multi-layer perceptron feed forward neural network component, coupled with the transceiver, configured to estimate parameters of multipath reflections using representations of the channel estimates or beamforming feedback, and to generate transmission correction factors for the transceiver.