Abstract: A voice-activated system edge device cooperating with a remote command processor has a state machine defined by a listening mode state and a conversation monitoring mode state. The state machine transitions from the listening mode state to the conversation monitoring mode state in response to a wake word detection. A command accompanying the wake word is transmitted to the remote command processor for execution thereon. The conversation monitoring mode state is maintained for a conversation monitoring window time duration to receive a connection word accompanied by another command transmitted to the remote command processor for further execution thereon.

Abstract: A system for configuring user-defined recognition patterns at an edge device using a hybrid cloud-edge device approach has a pattern recognition integrated circuit implementing a machine learning pattern recognizer that generates an event recognition output in response to an input thereto based upon pre-trained machine learning weights stored in a memory of the pattern recognition integrated circuit. A remote pattern recognition training service is in communication with a secondary user device receptive to a training input of the user-defined recognition patterns, and returns a set of training weights corresponding to the training input. An application interface connects the pattern recognition integrated circuit to the secondary user device, with the set of training weights returned to the secondary user device being transferable to the machine learning pattern recognizer for storage in the memory of the pattern recognition integrated circuit.

Abstract: A configurable monitoring and actioning system has one or more programmable edge devices each including a machine learning pattern recognizer, a sensor providing sensor input data to the pattern recognizer, and a memory storing pre-trained machine learning weight values for the pattern recognizer. Event detections are generated based upon evaluations of the sensor input data from the sensor against the pre-trained machine learning weight values. An application installable on a user device is in communication with each of the one or more programmable edge devices and executes predetermined actions based upon the event detection evaluations from the machine learning pattern recognizer.

Abstract: Discriminating between direct and machine-generated human voices is disclosed. A directly-generated voice audio sample from a human utterance and a machine-generated voice audio sample outputted by a loudspeaker from a pre-recording of another human utterance are captured on a microphone. Discriminative features between the directly-generated voice audio sample and the machine-generated voice audio sample are extracted with a machine learning classifier. A response to a command in the captured directly-generated voice audio sample or the captured machine-generated voice audio sample may be selectively generated.

Abstract: A deep learning training and inference system for a primary machine learning system has an automated data collection tool receptive to incoming input data from a sensor data source, and embeds one or more sensor data classifications associated with the incoming input data. A data augmentation tool is receptive to the input data from the automated data collection tool and generates an augmented input data set resulting from one or more predefined operations applied to the input data. An adaptive training tool is receptive to the augmented input data set to improve performance, with a new set of weight values being generated for the primary machine learning system. An inference tool is in communication with the adaptive training tool to receive the new set of weight values for an inference model simulator emulating a native hardware environment of the primary machine learning system.

Abstract: An always-on local action controller has one or more sensors each receptive to an external input. The respective external inputs are translatable to corresponding signals. One or more always-on data analytic neural network subsystems are each connected to a respective one of the sensors and are receptive to the signals outputted therefrom. An event detection is raised by a given one of the always-on data analytical neural network subsystems in response to a pattern of signal data corresponding to an event. A decision combiner is connected to each of the one or more always-on data analytic neural network subsystems, which generates an action signal based upon an aggregate of the events.

Abstract: A device wake-up system has one or more sensors each receptive to an external input. The respective external inputs are translatable to corresponding signals. One or more feature extractors connected to a respective one of the one or more sensors are receptive to the signals outputted from the sensors, and the feature data is associated with the signals being generated by the corresponding one of the one or more feature extractors. One or more inference circuits are connected to a respective one of the one or more feature extractors, and inference decisions are generated from patterns of the feature data generated by a corresponding one of the one or more feature extractors. A decision combiner is connected to each of the one or more inference circuits, and a wake signal is be generated based upon an aggregate of the inference decisions provided by the one or more inference circuits.

Abstract: A voice-activated system edge device cooperating with a remote command processor has a state machine defined by a listening mode state and a conversation monitoring mode state. The state machine transitions from the listening mode state to the conversation monitoring mode state in response to a wake word detection. A command accompanying the wake word is transmitted to the remote command processor for execution thereon. The conversation monitoring mode state is maintained for a conversation monitoring window time duration to receive a connection word accompanied by another command transmitted to the remote command processor for further execution thereon.

Abstract: A neural network parameter tuner has an auxiliary neural network receptive to an input data stream with signal components and noise components associated with ambient conditions. An ambient classification value is periodically derived from the input data stream based upon the noise components detected therein. A primary neural network receptive to the input data stream classifies the input data stream based upon an assigned detection threshold corresponding to the ambient classification value.

Abstract: A multi-stage selectable neural network noise suppression system has a first stage noise pattern selection neural network receptive to an input signal. An automatic noise classification is generated based upon an evaluation of the input signal. A noise suppression weight table stores one or more sets of automatic noise suppression weight values corresponding to the generated automatic noise classifications. A second stage noise pattern suppression neural network then selectively applies a specific automatic targeted noise suppression based upon the automatic noise suppression weight values. This approach balances performance quality and power/memory usage that efficiently tailors noise detection and suppression.

Abstract: There is provided a method of transcoding an Enhance Full Rate (EFR) 12.2 Kbps encoded frame into an Adaptive Multi-Rate (AMR) 12.2 Kbps encoded frame, where the method comprises receiving the EFR 12.2 Kbps encoded frame from a first codec; determining if the EFR 12.2 Kbps encoded frame is a Silence Insertion Descriptor (SID) frame; if the EFR 12.2 Kbps encoded frame is determined to be the SID frame, the method further comprises transcoding the EFR SID frame. There is also provided a method of transcoding an EFR 12.2 Kbps encoded frame into an AMR 12.2 Kbps encoded frame, where the method comprises receiving the AMR 12.2 Kbps encoded frame from a first codec; determining if the AMR 12.2 Kbps encoded frame is an SID frame; if the AMR 12.2 Kbps encoded frame is determined to be the SID frame, the method further comprises transcoding the AMR SID frame.

Abstract: There is provided a method of transcoding an Enhance Full Rate (EFR) 12.2 Kbps encoded frame into an Adaptive Multi-Rate (AMR) 12.2 Kbps encoded frame, where the method comprises receiving the EFR 12.2 Kbps encoded frame from a first codec; determining if the EFR 12.2 Kbps encoded frame is a Silence Insertion Descriptor (SID) frame; if the EFR 12.2 Kbps encoded frame is determined to be the SID frame, the method further comprises transcoding the EFR SID frame. There is also provided a method of transcoding an EFR 12.2 Kbps encoded frame into an AMR 12.2 Kbps encoded frame, where the method comprises receiving the AMR 12.2 Kbps encoded frame from a first codec; determining if the AMR 12.2 Kbps encoded frame is an SID frame; if the AMR 12.2 Kbps encoded frame is determined to be the SID frame, the method further comprises transcoding the AMR SID frame.