Cypress Semiconductor Corporation

Aidan Smyth

Kiran Uln

Victor Simileysky

Kamesh Medapalli

Zhuohui Zhang

Daniel Watson

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.

Systems, methods, and devices for wakeup word detection with continuous learning

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.

Leveraging spectral diversity for machine learning-based estimation of radio frequency signal parameters

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.

Machine learning-based antenna array validation, prototyping and optimization

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.

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Milpitas, CA, United States of America

Aidan Smyth