An airborne acoustic vector sensor for simultaneously measuring triaxial particle acceleration in three dimensions and pressure includes a triaxial MEMS accelerometer sensitive to an Earth gravitational field. The airborne acoustic vector sensor includes one or multiple MEMS microphones sensitive to sound pressure and overlapping the accelerometer in frequency. The airborne acoustic vector sensor includes a solid body having a density approximating a density of air. The accelerometer is mounted in or upon the solid body. The airborne acoustic vector sensor includes a suspension system supporting the accelerometer and solid body within a framework.

A method for detecting blowout precursors in at least one gas turbine combustor comprising: receiving combustion dynamics acoustic data measured by an acoustic measuring device associated with the combustor in real time; performing wavelet analysis on the acoustic data using simplified Mexican Hat wavelet transform analysis; and determining the existence of a blowout precursor based at least in part on the wavelet analysis. Provided also is a system and a non-transitory computer readable medium configured to perform the method.

A method for detecting blowout precursors in at least one gas turbine combustor comprising: receiving combustion dynamics acoustic data measured by an acoustic measuring device associated with the combustor in real time; performing wavelet analysis on the acoustic data using simplified Mexican Hat wavelet transform analysis; and determining the existence of a blowout precursor based at least in part on the wavelet analysis. Provided also is a system and a non-transitory computer readable medium configured to perform the method.

Embodiments are provided for automatically determining the power state of a consumer electronic device, such as a television. For instance, a switching device may transmit a reference audio signal to a consumer electronic device for playback thereby. An audio signal captured by a microphone is compared to a reference audio signal. If a level of similarity between the captured audio signal and the reference audio signal meet a threshold condition, then a determination is made that the consumer electronic device is powered on. Otherwise, a determination is made that the consumer electronic device is powered off, in a standby state or muted.

An assessment system includes a sound data acquiring unit configured to acquire sound data collected by a sound insulation device at a predetermined assessment location, the sound insulation device being fit into a wearer's ears; and a noise map generator configured to generate a noise map representing a distribution of a sound pressure level at the predetermined assessment location, based on the sound data acquired by the sound data acquiring unit and position information associated with the sound data.

According to one embodiment, a sensor includes first and second structure bodies, and a detector. The first structure body includes a supporter, and first and second deformable parts supported by the supporter. The second deformable part has at least one of a second length less than a first length of the first deformable part, a second width greater than a first width of the first deformable part, a second thickness greater than a first thickness of the first deformable part, a second Young's modulus greater than a first Young's modulus of the first deformable part, or a second spring constant greater than a first spring constant of the first deformable part. The second structure body is connected to the first structure body. A liquid is provided between the first and second structure bodies. The detector outputs a signal corresponding to a deformation of the first or second deformable part.

A signal acquisition unit of an abnormality detection device for a twin-screw extrusion molding machine, when the twin-screw extrusion molding machine that melts and kneads a fed resin raw material is in operation, acquires an AE output of an AE sensor installed on a surface of a housing of the twin-screw extrusion molding machine. A determination unit determines whether an abnormality occurs in the twin-screw extrusion molding machine, based on a relationship between the AE output and a first threshold and a second threshold.

Systems and methods for identifying potential bullying are disclosed. In various aspects, a system for identifying potential bullying includes a sound detector configured to provide samples of sounds over time, a processor, and a memory storing instructions. The instructions, when executed by the processor, cause the system to determine that a noise event has occurred by processing the samples to determine that the sounds exceed a sound level threshold over a time period that exceeds a time period threshold, process the samples to provide frequency spectrum information of the noise event, determine whether the noise event is a potential bullying occurrence based on comparing the frequency spectrum information of the noise event and at least one frequency spectrum profile, and initiate a bullying notification in a case of determining that the noise event is a potential bullying occurrence.

An IoT Smart Sound Level Meter that is connected through the internet via ethernet cable, wifi or cellular network. The IoT Smart Sound Level Meter measures and monitors sound pressure levels. Sound pressure levels are measured in decibels. The sound pressure level data that is sent via the internet through ethernet, wifi or cellular network to cloud servers where the data is stored and saved. The cloud servers are connected to Application Programming Interfaces (API) connected to web-based software that converts the sound pressure level data into decibel measurements. These measurements are displayed to the end-user through the web-based software and/or application providing real-time decibel level measurements and historic sound pressure level recordings. The web-based software and/or application provides access to view decibel measurement data remotely from a computer, laptop, tablet, smart watch, or mobile device.

An IoT Smart Sound Level Meter that is connected through the internet via ethernet cable, wifi or cellular network. The IoT Smart Sound Level Meter measures and monitors sound pressure levels. Sound pressure levels are measured in decibels. The sound pressure level data that is sent via the internet through ethernet, wifi or cellular network to cloud servers where the data is stored and saved. The cloud servers are connected to Application Programming Interfaces (API) connected to web-based software that converts the sound pressure level data into decibel measurements. These measurements are displayed to the end-user through the web-based software and/or application providing real-time decibel level measurements and historic sound pressure level recordings. The web-based software and/or application provides access to view decibel measurement data remotely from a computer, laptop, tablet, smart watch, or mobile device.