A signal processing method and device of a MEMS microphone and a MEMS microphone are disclosed. The method comprises: acquiring a first electrical signal obtained by converting a received optical signal by an optical sensor disposed near a sound hole of the MEMS microphone; acquiring a second electrical signal output by the MEMS microphone; and judging that the second electrical signal is an interference signal when within a substantially overlapping time range, the first electrical signal acquired reaches a preset first threshold value and the second electrical signal acquired meets a preset condition.

A method comprises outputting at least one test signal to a listening environment via a loudspeaker, receiving each of the at least one test signal via a microphone arranged in the listening environment, determining at least one of a position and an orientation of the microphone in the listening environment with respect to the loudspeaker at the time of receipt of each of the at least one test signal, evaluating the test signals received by the microphone, and determining at least one aspect related to at least one transfer function based on the evaluation of the test signal and on at least one of the position and the orientation of the microphone.

The present application relates to a human-machine interaction method and device, a computer apparatus, and a storage medium. The method comprises: measuring the current output volume, and if the output volume is less than a first preset threshold, enabling a voice recognition function; acquiring a user's voice message, and measuring the size of the user's voice volume and responding to a user's voice operation; and if the user's voice volume is greater than a second preset threshold, turning down the output volume, and returning to the step of measuring the current output volume. In the entire process, the voice recognition function is controlled to be enabled by means of the output volume of an apparatus itself, thereby accurately responding to the user's voice operation, and if the user's voice is greater than a specified value, turning down the output volume, so that a user's subsequent voice message can be highlighted and accurately acquired so as to bring convenience to a user's operation and implement good human-machine interaction.

An acquisition system includes a processor, one or more sensors operatively coupled to the processor where the one or more sensors acquire at the ear, on the ear or within an ear canal, one or more of acceleration, blood oxygen saturation, blood pressure or heart-rate, and the one or more sensors configured to monitor a biological state or a physical motion or both for an event. The event can be a detection of a discrepancy when compared with a set of reference data by the one or more sensors or the biological state or the event can be one of a detection of an abrupt movement of a headset operatively coupled to the processor, a change in location of an earpiece operatively coupled the processor, a touching of the headset, a recognizing of a voice command, a starting or ending of a phone call, or a scheduled time.

Systems, apparatuses, and methods are described for a privacy blocking device configured to prevent receipt, by a listening device, of video and/or audio data until a trigger occurs. A blocker may be configured to prevent receipt of video and/or audio data by one or more microphones and/or one or more cameras of a listening device. The blocker may use the one or more microphones, the one or more cameras, and/or one or more second microphones and/or one or more second cameras to monitor for a trigger. The blocker may process the data. Upon detecting the trigger, the blocker may transmit data to the listening device. For example, the blocker may transmit all or a part of a spoken phrase to the listening device.

A system for detecting parasitic oscillation in a wearable audio device that includes an electro-acoustic transducer that is configured to develop sound for a user, a housing that holds the transducer, at least one of a feedforward microphone that is configured to detect sound outside of the housing and output a feedforward microphone signal and a feedback microphone that is configured to detect sound inside of the housing and output a feedback microphone signal, and an opening in the housing that emits sound pressure from the transducer. The system includes a parasitic oscillation detector that is configured to determine a fundamental frequency of at least one of the feedforward and feedback microphone signals and compare an amplitude of the determined fundamental frequency to a threshold level, to determine parasitic oscillation.

Provided a robot for assisting hearing of a user, while minimizing an influence on the surroundings. The robot includes a speaker, a microphone configured to recognize a voice, a processor configured to acquire a position of a user's face when a hearing aid command is acquired on the basis of the voice recognized through the microphone, and a driving unit configured to cause the speaker to be moved toward the position of the user's face, wherein the processor acquires a sound which is a target of hearing aid, generates an assistant sound by amplifying a predetermined frequency band of the sound or converting the predetermined frequency band of the sound into a different frequency band, and outputs the assistant sound through the speaker.

The embodiments of the present application provide an earphone abnormality processing method, an earphone, a system, and a storage medium. In the embodiments of the present application, an abnormal state self-detection function is added to an earphone of an existing dual-microphone, and when the earphone is in a self-detection state, acquiring a sound signal picked up from a specified sound source by a primary microphone and a secondary microphone; and determining the type of abnormal state of the earphone according to a frequency response curve of the sound signal picked up by the primary microphone and the secondary microphone, and further performing abnormality processing on the earphone by using a processing manner adapted to the abnormal state of the earphone, thereby solving the difficulty in the prior art of being unable to process the sound pickup abnormality of an earphone, and improving the usage performance of an earphone and also facilitating the prolonging of the service life of the earphone.

Exemplary multipath digital microphones described herein can comprise exemplary embodiments of automatic gain control and multipath digital audio signal digital signal processing chains, which allow low power and die size to be achieved as described herein, while still providing a high DR digital microphone systems. Further non-limiting embodiments can facilitate switching between multipath digital audio signal digital signal processing chains while minimizing audible artifacts associated with either the change in the gain automatic gain control amplifiers switching between multipath digital audio signal digital signal processing chains.

Disclosed is a speech processing method. The speech processing method controls activation timing of a microphone based on a response pattern of the microphone from a user in order to implement a natural conversation. The speech processing device and the NLP system of the present disclosure may be associated with an artificial intelligence module, a drone (or unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to 5G service, etc.