Broadband planar nearfield acoustic holography based on one-third-octave band analysis

Zhou, Huan; Lopez-Arteaga, Ines; Nijmeijer, Henk

doi:10.1016/j.apacoust.2016.02.002

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Besides, the cracked sound pulses randomly have a sudden change in some frequency bands, as shown in Figure 3 . This can be roughly represented in terms of the 1/6 octave frequency band, with a lower frequency,

, and a higher frequency,

, where

is the centroid frequency [ 15 ]. The sound pressure level (SPL) of the 1/6 octave frequency band is measured in decibels and defined as

where

is the measured sound pressure in

;

is the reference sound pressure with a value of

Pa in air.…”

Section: The Proposed Crack Detection Methodsmentioning

confidence: 99%

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Chen

Wang

Zeng

2018

Sensors

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Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.

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, and a higher frequency,

, where

is the centroid frequency [ 15 ]. The sound pressure level (SPL) of the 1/6 octave frequency band is measured in decibels and defined as

where

is the measured sound pressure in

;

is the reference sound pressure with a value of

Pa in air.…”

Section: The Proposed Crack Detection Methodsmentioning

confidence: 99%

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Chen

Wang

Zeng

2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Then, one-third octave spectra are adopted in this research to emphasize the information in the low-frequency part of the speech signal [65]. In one-third octave spectra, frequency components ranging from 0 to 22,050 Hz are divided into 43 frequency bands [66]. These frequency bands in the lower frequency part are densely distributed with shorter bandwidths than those in the higher frequency part.…”

Section: The Pearson Correlation Coefficient Calculation For the Highmentioning

confidence: 99%

Acoustic analysis and detection of pharyngeal fricative in cleft palate speech using correlation of signals in independent frequency bands and octave spectrum prominent peak

Wang

Yin

et al. 2020

BioMed Eng OnLine

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Background: Pharyngeal fricative is one typical compensatory articulation error of cleft palate speech. It passively influences daily communication for people who suffer from it. The automatic detection of pharyngeal fricatives in cleft palate speech can provide information for clinical doctors and speech-language pathologists to aid in diagnosis. Results: This paper proposes two features (CSIFs: correlation of signals in independent frequency bands; OSPP: octave spectrum prominent peak) to detect pharyngeal fricative speech. CSIFs feature is proposed to detect the distribution characteristics of frequency components in pharyngeal fricative speech caused by the changed place of articulation and movement of articulators. While OSPP is presented to reflect the concentration degree of prominent peak which is closely related to the place of articulation in pharyngeal fricative, both features are investigated to relate to the altered production process of pharyngeal fricative. To evaluate the capability of these two features to detect pharyngeal fricative, we collected a speech database covering all the types of initial consonants in which pharyngeal fricatives occur. In this detection task, the classifier used to discriminate pharyngeal fricative speech and normal speech is based on ensemble learning. Conclusion: The detection accuracy obtained with CSIFs and OSPP features ranges from 83.5 to 84.5% and from 85 to 87%, respectively. When these two features are combined, the detection accuracy for pharyngeal fricative speech ranges from 88 to 89%, with an AUC (area under the receiver operating characteristic curve) value of 93%.

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Hybrid patch near-field acoustic holography based on Kalman filter

2019

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Broadband planar nearfield acoustic holography based on one-third-octave band analysis

Cited by 5 publications

References 19 publications

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Acoustic analysis and detection of pharyngeal fricative in cleft palate speech using correlation of signals in independent frequency bands and octave spectrum prominent peak

Hybrid patch near-field acoustic holography based on Kalman filter

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