Detection and Feature Extraction in Acoustic Sensor Signals

• Other Authors: Li, Yuxing (Editor), Fredianelli, Luca (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Technology: general issues; History of engineering & technology; Scholte wave detection; multilayer elastic bottom; acoustic pressure field; source depth; propagation distance; feature extraction; target recognition; neural networks; underwater acoustic signals; acoustic ranging; acoustic thermometry; digital lock-in filtering; electrical conduit; time-of-flight estimation; delay estimation; singular value decomposition; GCC-PHAT-ργ weighting; generalized cross-correlation; SPB method; sound pass-by; low-noise surfaces; noise modeling; road traffic noise; unattended noise measurement procedure; traffic measurements; noise emission; environmental noise; sound; partial updates; least mean squares; Leaky LMS; structural active noise control; deconvolved beamforming; fractional Fourier transform; direction of arrival estimation; linear frequency modulation signal; fault diagnosis; hierarchical slope entropy; white shark optimizer; optimized support vector machine; bearing signals; metro traction motor bearings; multisignal fusion; Markov transition field; optimized deep residual network; diagnosis of compound faults; multistable stochastic resonance; adaptive parameter; improved grey wolf algorithm; bearing fault detection; state estimation; unknown statistical characteristics of noise; cost-reference particle filter; multi-population cooperation; intelligent resample; Gaussian mutation; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

Acoustic sensors have an extremely wide range of applications in many fields, including underwater acoustics, architectural acoustics, engineering acoustics, physical acoustics, environmental acoustics, psychoacoustics, and so on. The signals collected by high-sensitivity acoustic sensors contain a large amount of valid information that facilitates further processing of the collected acoustic signals. In particular, detection and feature extraction, as two important measures of acoustic sensor signal processing, can capture more information regarding the target and extract features with separability.