Disclosed embodiments are directed an application program configured to run on a user's mobile device can allow voice-activated call pick-up to the user, without the user having to use his or her hands for picking up the call. For example, the application program can initially be trained to a user's voice command. When an incoming call is received at the mobile device, the user can pick up the call by issuing a voice command. In some embodiments, the application program can determine whether to allow voice-activated pick-up of calls based on data collected from multiple sensors associated with the vehicle, the mobile device, or a remote source.

A terminal control method, a terminal and a non-transitory computer-readable storage medium are provided. The terminal control method includes: receiving, by a microphone, a detection audio signal emitted from a speaker and having a frequency within a pre-set detection frequency range; acquiring actual audio parameters of the detection audio signal when being received by the microphone, and original audio parameters of the detection audio signal when being emitted from the speaker; determining a relative state between the microphone and the speaker according to the actual audio parameters and the original audio parameters; determining a terminal control operation to be performed, according to the relative state and a pre-set correspondence between relative states and terminal control operations; and performing the determined terminal control operation on a terminal where the microphone is located.

A terminal control method, a terminal and a non-transitory computer-readable storage medium are provided. The terminal control method includes: receiving, by a microphone, a detection audio signal emitted from a speaker and having a frequency within a pre-set detection frequency range; acquiring actual audio parameters of the detection audio signal when being received by the microphone, and original audio parameters of the detection audio signal when being emitted from the speaker; determining a relative state between the microphone and the speaker according to the actual audio parameters and the original audio parameters; determining a terminal control operation to be performed, according to the relative state and a pre-set correspondence between relative states and terminal control operations; and performing the determined terminal control operation on a terminal where the microphone is located.

Described herein is a MEMS acoustic transducer device provided with a micromechanical detection structure that detects acoustic-pressure waves and supplies a transduced electrical quantity, and with an integrated circuit operatively coupled to the micromechanical detection structure and having a reading module that generates at output an audio signal as a function of the transduced electrical quantity. The integrated circuit is further provided with a recognition module, which recognizes a of sound activity event associated to the transduced electrical quantity. The MEMS acoustic transducer has an output that supplies at output a data signal that carries information regarding recognition of the sound activity event.

An image pickup apparatus includes an image pickup unit that obtains video, an audio input unit that collects sound and a control unit that controls recording of the video based on a wake word and a control word included in the sound collected by the audio input unit. In a case where the control word that gives an instruction to stop recording the video is included in the sound, the control unit stops recording the video and records video data before a start time of the wake word as a video file.

Methods and apparatus to detect spillover are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to: identify a quantity of first durations of loudness in an audio signal of media; calculate a ratio of the quantity of the first durations of loudness to a quantity of second durations of loudness in the audio signal of the media, the quantity of the second durations of loudness including the quantity of the first durations of loudness; and in response to a detection of the audio signal being spillover, store data denoting the media as un-usable to credit a media exposure when the ratio does not satisfy a loudness ratio threshold, the storing of the data to improve an accuracy of media exposure credits by not crediting spillover media.

A wearable device can provide an audio module that is operable to provide audio output from a distance away from the ears of the user. For example, the wearable device can be worn on clothing of the user and direct audio waves to the ears of the user. Such audio waves can be focused by a parametric array of speakers that limit audibility by others. Thus, the privacy of the audio directed to the user can be maintained without requiring the user to wear audio headsets on, over, or in the ears of the user. The wearable device can further include microphones and/or connections to other devices that facilitate calibration of the audio module of the wearable device. The wearable device can further include user sensors that are configured to detect, measure, and/or track one or more properties of the user.

A wearable device can provide an audio module that is operable to provide audio output from a distance away from the ears of the user. For example, the wearable device can be worn on clothing of the user and direct audio waves to the ears of the user. Such audio waves can be focused by a parametric array of speakers that limit audibility by others. Thus, the privacy of the audio directed to the user can be maintained without requiring the user to wear audio headsets on, over, or in the ears of the user. The wearable device can further include microphones and/or connections to other devices that facilitate calibration of the audio module of the wearable device. The wearable device can further include user sensors that are configured to detect, measure, and/or track one or more properties of the user.

An abnormality degree calculation system includes: a feature amount vector extraction unit configured to generate and output a feature amount vector from an input signal originating from vibration of a target device; an encoding unit configured to receive as an input a set composed of the feature amount vector and a device type vector representing a type of the target device and output an encoding vector; a decoding unit configured receive as an input the encoding vector and the device type vector and output a decoding vector; a learning unit configured to learn parameters of the neural networks of the encoding unit and the decoding unit; and an abnormality degree calculation unit configured to calculate a degree of abnormality defined as a function of the feature amount vector from the feature amount vector extraction unit, the encoding vector from the encoding unit, and the decoding vector from the decoding unit.

An abnormality degree calculation system includes: a feature amount vector extraction unit configured to generate and output a feature amount vector from an input signal originating from vibration of a target device; an encoding unit configured to receive as an input a set composed of the feature amount vector and a device type vector representing a type of the target device and output an encoding vector; a decoding unit configured receive as an input the encoding vector and the device type vector and output a decoding vector; a learning unit configured to learn parameters of the neural networks of the encoding unit and the decoding unit; and an abnormality degree calculation unit configured to calculate a degree of abnormality defined as a function of the feature amount vector from the feature amount vector extraction unit, the encoding vector from the encoding unit, and the decoding vector from the decoding unit.