Embodiments of the present application disclose a method and an apparatus for obtaining vibration information and a piece of user equipment. The method comprises: obtaining multiple groups of electromagnetic wave receiving signals corresponding to multiple receiving locations; obtaining, according to transmission channel information between the multiple receiving locations and the multiple reflective units and the multiple groups of electromagnetic wave receiving signals, multiple reflected electromagnetic wave signals respectively corresponding to the multiple reflective units; and obtaining, according to the multiple reflected electromagnetic wave signals, multiple pieces of vibration information respectively corresponding to the multiple reflective units. In technical solutions of the embodiments of the present application, obtaining sound information in an environment by using a wireless electromagnetic wave is especially applicable to some scenarios where an active component is not suitable for use at a sound collecting location and a scenario where sound information at multiple locations needs to be collected in a space.

Some systems include an acoustic sensor array configured to receive acoustic signals, an electromagnetic imaging tool configured to receive electromagnetic radiation, a user interface, a display, and a processor. The processor can receive electromagnetic data from the electromagnetic imaging tool and acoustic data from the acoustic sensor array. The processor can generate acoustic image data of the scene based on the received acoustic data, generate a display image comprising combined acoustic image data and electromagnetic image data, and present the display image on the display. The processor can receive an annotation input from the user interface and update the display image based on the received annotation input. The processor can be configured to determine one or more acoustic parameters associated with the received acoustic signal and determine a criticality associated with the acoustic signal. A user can annotated the display image with determined criticality information or other determined information.

Systems and method for providing an audio output with an acoustic intensity analyzer. The acoustic intensity analyzer includes an acoustic intensity array, a controller, and an orientation sensor. The acoustic intensity array is fixed to the acoustic intensity analyzer. The controller is coupled to the acoustic intensity array to produce the audio output using acoustic holography based on an input from the acoustic intensity array. The controller is configured to set an aim of the acoustic holography in a selected direction. The orientation sensor is coupled to the controller and mechanically fixed to the acoustic intensity analyzer such that there is no relative movement between the orientation sensor and the acoustic intensity array. The orientation sensor detects a change in an orientation of the acoustic intensity array and provides an orientation signal to the controller for adjusting the aim of the acoustic holography to maintain the selected direction.

Sensor devices, systems, and methods for measuring different components of a flow are provided. A sensing arrangement includes a substrate and first and second sensor arrays on the substrate. The first sensor array sensing elements are distributed to obtain measurement data indicative of a first property of an operating environment, such as a turbulent component of a fluid flow. The second sensor array sensing elements are interspersed amongst the first sensor array and distributed to obtain measurement data indicative of a second property of the operating environment, such as an acoustic component of the fluid flow.

Embodiments of the present disclosure relate to an AI-based noise generation diagnosis apparatus and method of diagnosing noise of PE components for an electric vehicle, in which a plurality of microphones are disposed in a vehicle interior, and a calculation unit measures positions of noise sources from any one or more among the PE components comprising a main motor, a decelerator, an inverter, and an auxiliary motor from front, rear, and upward directions of the microphone, and at the same time, estimates a noise transmission position where the noise is transmitted.

A method for extracting and correcting a ringing component in a dynamic pressure measurement signal of a shock wave flow field includes: adaptively decomposing a dynamic pressure measurement signal of a shock wave flow field through an empirical mode decomposition (EMD) method, and extracting a ringing component in the dynamic pressure measurement signal of the shock wave flow field; constructing a ringing component distortion correction model through a convolutional neural network (CNN) and a dataset through a staggered spanning dataset construction method; and predicting a time series of a segment with mode mixing in the ringing component, to accurately extract a ringing component in a dynamic pressure response signal. The method combines advantages of an adaptive signal decomposition method and a CNN model identification method.

A system for dynamic noise compliance is provided. The system includes a computer device programmed to: a) store a plurality of information about a plurality of parameters for one or more noise studies; b) store one or more models of noise emissions; c) receive updated information about a first noise study; d) execute the one or more models of noise emissions with the updated information as inputs to generate an updated model of noise emissions for the first noise study; e) generate a first map overlay for the first noise study based upon the updated model of noise emissions for the first noise study; and/or f) transmit instructions to a user computer device to cause a map with the first map overlay to be displayed on a display device of the user computer device.

A sound field measuring device (1) obtains frequency characteristics by collecting output sound outputted from a pair of speakers (101a, 101b) installed at a narrow interval. A low-pass filter (22a) extracts low-range components of a first measurement signal. A high-pass filter (22b) extracts mid/high-range components of a second measurement signal different from the first measurement signal. A combined signal generation unit (22c) generates a combined signal by combining the low-range components of the first measurement signal and the mid/high-range components of the second measurement signal. An external output unit (6) outputs the first measurement signal to an audio system (102). A microphone (7) collects the first measurement signal and the combined signal simultaneously outputted from the pair of speakers. A Fourier transform unit (13) obtains the frequency characteristics of a sound field by Fourier transforming the signals collected.

A horizontal acoustic vector sensor system described herein includes a housing which has a gimbal assembly therein which is attached to a sensor assembly which has multiple pairs of seismometers that arranged orthogonally to one or more neighboring pairs of seismometers, along an approximately horizontal axis. The gimbal assembly with sensor assembly are enclosed within the housing by an endcap which includes an electronics assembly. The multiple pairs of seismometers are wired to the electronics assembly through a slip-ring which allows for movement of the gimbal assembly without entangling the wires. The horizontal acoustic vector sensor system further includes at least one omni-directional hydrophone integrated into the endcap.

A method for extracting and correcting a ringing component in a dynamic pressure measurement signal of a shock wave flow field includes: adaptively decomposing a dynamic pressure measurement signal of a shock wave flow field through an empirical mode decomposition (EMD) method, and extracting a ringing component in the dynamic pressure measurement signal of the shock wave flow field; constructing a ringing component distortion correction model through a convolutional neural network (CNN) and a dataset through a staggered spanning dataset construction method; and predicting a time series of a segment with mode mixing in the ringing component, to accurately extract a ringing component in a dynamic pressure response signal. The method combines advantages of an adaptive signal decomposition method and a CNN model identification method.