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Journal articles on the topic 'Cepstrum'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Nowicki, Andrzej, and Artur Marciniak. "Detection of Wall Vibrations by Means of Cepstrum Analysis." Ultrasonic Imaging 11, no. 4 (1989): 273–82. http://dx.doi.org/10.1177/016173468901100405.

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Preliminary studies on the usefulness of the cepstrum analysis of Doppler signals for detecting vibrations of periodically vibrating structures, such as aortic valves in children with innocent heart murmurs, were carried out in two stages. First, by cepstrum analysis of computer simulated frequency modulated signals, and second, by the analysis of real Doppler signals generated by a vibrating reflector. Simulation and experimental studies were performed for modulating frequencies from 10 Hz to 60 Hz while changing the amplitude of vibrations from 0.2 mm to 1 mm. The striking similarity between

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2

Li, Zong Tao, Yan Gao, Xiang Zhou, and Yu Guo. "Feature Extraction of Faulty Rolling Element Bearing Based on Time Synchronous Average and Cepstrum Edit." Advanced Materials Research 889-890 (February 2014): 666–70. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.666.

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The cepstrum edit scheme for the vibration feature extraction of the faulty rolling element bearing (REB) is studied in this paper. By combined the time synchronous average (TSA) and the real cepstrum to localize and edit the cepstral lines of the original vibration, the unwanted discrete frequency components can be removed. Then, a corresponding inverse procedure is designed, in which the edited cepstrum and the original phase spectrum are employed to reconstruct the edited vibration for the REB feature extraction. Simulation verified the scheme positively.

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3

Kamath, Chandrakar. "Comparison of Baseline Cepstral Vector and Composite Vectors in the Automatic Seizure Detection Using Probabilistic Neural Networks." ISRN Biomedical Engineering 2013 (August 27, 2013): 1–9. http://dx.doi.org/10.1155/2013/984864.

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Epileptic seizures are abnormal sudden discharges in the brain with signatures manifesting in the electroencephalogram (EEG) recordings by frequency changes and increased amplitudes. These changes, in this work, are captured through traditional cepstrum and the cepstrum-derived dynamic features. We compared the performance of the traditional baseline cepstral vector with that of the two composite vectors, the first including velocity cepstral coefficients and the second including velocity and acceleration cepstral coefficients, using probabilistic neural network in general epileptic seizure de

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4

Zheng, G. T., and W. J. Wang. "A New Cepstral Analysis Procedure of Recovering Excitations for Transient Components of Vibration Signals and Applications to Rotating Machinery Condition Monitoring." Journal of Vibration and Acoustics 123, no. 2 (2001): 222–29. http://dx.doi.org/10.1115/1.1356696.

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A new cepstral analysis procedure with the complex cepstrum for recovering excitations causing multiple transient signal components from vibration signals, especially from rotor vibration signals, has been developed. Along with the problem of singularity, a major problem of the cepstrum is that it cannot provide a correct distribution of the excitations. To solve these problems, a signal preprocessing method, whose function is to provide a definition for the distribution of the excitations along the quefrency axis and remove singular points from the transform, has been added to the cepstrum an

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5

Toumi, Yassine, Billel Bengherbia, Mohamed Rebiai, and Zmirli Mohamed Ould. "Bearing faults diagnosis using cepstral analysis and 1D Convolutional neural network." AINTELIA SCIENCE NOTES 1, no. 1 (2022): 59–64. https://doi.org/10.5281/zenodo.8070908.

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The diagnosis of faults in the rotating machines has become necessary recently, in the order to ensure their safety and efficiency. the rolling bearing is one of the most components prone to failure in the rotating machines. In this work, we propose a novel approach to detecting and classifying the rolling bearing faults by using the cepstral analysis and 1D-CNN. First, the real, complex and power cepstrum are calculated, which are later used as input to the classifier. Second, a 1D-CNN is used as a classifier to diagnose the bearing faults. The proposed method is tested on the CWRU dataset fr

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6

Reddy, Anil Kundhur. "Cepstrum Analysis." IETE Technical Review 7, no. 2 (1990): 133–36. http://dx.doi.org/10.1080/02564602.1990.11438604.

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7

Reddy, G. R., and V. V. Rao. "On the computation of complex cepstrum through differential cepstrum." Signal Processing 13, no. 1 (1987): 79–83. http://dx.doi.org/10.1016/0165-1684(87)90113-7.

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8

Hu, Teng, Ru Zhang, and Jian Yi Liu. "An Audio Zero-Watermarking Algorithm Based on Wavelet and Cepstrum Coefficients Mean Comparison." Advanced Materials Research 811 (September 2013): 508–13. http://dx.doi.org/10.4028/www.scientific.net/amr.811.508.

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This paper presented an audio zero-watermarking algorithm based on wavelet and cepstrum coefficients mean comparison. The Algorithm first divided the original audio into four parts, and then used the wavelet transform to conduct 3-level wavelet decomposition in every audio signal, extracting the approximate coefficients of the third layer wavelet to conduct cepstrum transform according to fixed length segment,and removing the part with high fluctuation at both ends of the cepstral coefficients, selecting the stable middle part to construct the watermark based on the two neighboring mean value

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9

Nirmal, Jagannath, Suprava Patnaik, Mukesh Zaveri, and Pramod Kachare. "Complex Cepstrum Based Voice Conversion Using Radial Basis Function." ISRN Signal Processing 2014 (February 6, 2014): 1–13. http://dx.doi.org/10.1155/2014/357048.

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The complex cepstrum vocoder is used to modify the speaker specific characteristics of the source speaker speech to that of the target speaker speech. The low time and high time liftering are used to split the calculated cepstrum into the vocal tract and the source excitation parameters. The obtained mixed phase vocal tract and source excitation parameters with finite impulse response preserve the phase properties of the resynthesized speech frame. The radial basis function is explored to capture the nonlinear mapping function for modifying the complex cepstrum based real and imaginary compone

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10

Braun, S. "Cepstrum based methods." Mechanical Systems and Signal Processing 128 (August 2019): 674–76. http://dx.doi.org/10.1016/j.ymssp.2019.01.039.

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11

Sandberg, Johan, and Maria Hansson-Sandsten. "Optimal cepstrum smoothing." Signal Processing 92, no. 5 (2012): 1290–301. http://dx.doi.org/10.1016/j.sigpro.2011.11.026.

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12

Motazedi, Niloufar, and Stephen Beck. "Leak detection using cepstrum of cross-correlation of transient pressure wave signals." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 15 (2017): 2723–35. http://dx.doi.org/10.1177/0954406217722805.

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A new leak detection method is proposed here which is based on the cepstrum of the cross-correlation of the pressure signals from two transducers. Computational simulations of leaks with different properties, size, position and shape, in a straight pipe and a T-Junction network were studied. The proposed method was successful in estimating leakages and the pipeline features with a high precision. For the results with a straight pipe, this method is considerably more accurate than using the cross-correlation leak detection method or the cepstrum method alone. However, the results obtained by ce

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13

Rorke, M. "Use of cepstrum analysis and cepstral imaging in characterizing liver tissue." Ultrasonic Imaging 7, no. 1 (1985): 80. http://dx.doi.org/10.1016/0161-7346(85)90019-7.

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14

Guo, Yan Jun, and Jie Han. "Analysis about Coordinate of Cepstrum and its Application in Gearbox Fault Diagnosis." Applied Mechanics and Materials 397-400 (September 2013): 2219–22. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2219.

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The definition of cepstrum analysis, diagnostic characteristics and the advantages of the method in gearbox fault diagnosis were introduced in this paper, and what are units and significance of horizontal and vertical coordinates of cepstrum was also analyzed , along with the cited examples of cepstrum analysis in gearbox fault diagnosis application .

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15

Guo, Yan Jun, and Jie Han. "Value of Cepstrum Analysis and its Application in Gearbox Fault Diagnosis." Advanced Materials Research 744 (August 2013): 83–86. http://dx.doi.org/10.4028/www.scientific.net/amr.744.83.

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A method for analyzing the value of cepstrum without taking the logarithm is proposed. The bottom of abscissa of cepstrum is cycle of power spectrum.Ordinate of the cepstrum can identify how many lines in the sideband of power spectrum without logarithmic.Application to fault diagnosis of the test bench and the gearbox is successfully, and thus significant results are achieved.

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16

Guo, Yan Jun, and Jie Han. "Value of Cepstrum Analysis and its Application in Gearbox Fault Diagnosis." Advanced Materials Research 753-755 (August 2013): 2196–99. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2196.

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A method for analyzing the value of cepstrum without taking the logarithm is proposed. The bottom of abscissa of cepstrum is cycle of power spectrum.Ordinate of the cepstrum can identify how many lines in the sideband of power spectrum without logarithmic.Application to fault diagnosis of the test bench and the gearbox is successfully, and thus significant results are achieved.

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17

Sanchez, Fabrício Lopes, Sylvio Barbon, Lucimar Sasso Vieira, et al. "Wavelet-based cepstrum calculation." Journal of Computational and Applied Mathematics 227, no. 2 (2009): 288–93. http://dx.doi.org/10.1016/j.cam.2008.03.016.

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18

Ma, Ying, and Feng Wei. "A Speech Signal Logarithmic Spectrum Amplitude Envelope Algorithm Analysis." Advanced Materials Research 756-759 (September 2013): 2072–75. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2072.

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Cepstrum method is a traditional characteristic parameter detection algorithm in the modern in the speech signal processing . it is one of the speech signal using the cepstrum features, to detect the representation glottis incentive cycle pitch information.In order to eliminate the influence of the fundamental frequency harmonic, we utilize the homomorphism solution volume technology, getting smooth spectral envelope.While the artical analyses cepstrum from out of the signal deleted to the short time window , detect speech signal pitch information;Then the signal of Fourier transform, take the

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19

Luo, Bing, Wen Tong Yang, Zhi Feng Liu, Yong Sheng Zhao, and Li Gang Cai. "Gear Fault Diagnosis Method Based on the Integration of Empirical Mode Decomposition and Cepstrum Analysis." Applied Mechanics and Materials 310 (February 2013): 328–33. http://dx.doi.org/10.4028/www.scientific.net/amm.310.328.

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Gear is the most common mechanical transmission equipment. Therefore, gear fault diagnosis is of much significance. In this article, a gear fault diagnosis method based on the integration of empirical mode decomposition and cepstrum is proposed by introducing empirical mode decomposition and cepstrum into gear fault analysis. Firstly EMD is used to decompose the gear vibration signal finite number of intrinsic mode functions and a residual error item. To do gear fault diagnosis, cepstrum analysis is carried upon those intrinsic mode functions to extract feature information from the vibration s

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20

Miao, Xiaokong, Meng Sun, Xiongwei Zhang, and Yimin Wang. "Noise-Robust Voice Conversion Using High-Quefrency Boosting via Sub-Band Cepstrum Conversion and Fusion." Applied Sciences 10, no. 1 (2019): 151. http://dx.doi.org/10.3390/app10010151.

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This paper presents a noise-robust voice conversion method with high-quefrency boosting via sub-band cepstrum conversion and fusion based on the bidirectional long short-term memory (BLSTM) neural networks that can convert parameters of vocal tracks of a source speaker into those of a target speaker. With the implementation of state-of-the-art machine learning methods, voice conversion has achieved good performance given abundant clean training data. However, the quality and similarity of the converted voice are significantly degraded compared to that of a natural target voice due to various f

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21

Rasyid, Muhammad Fahim, Herlina Jayadianti, and Herry Sofyan. "APLIKASI PENGENALAN PENUTUR PADA IDENTIFIKASI SUARA PENELEPON MENGGUNAKAN MEL-FREQUENCY CEPSTRAL COEFFICIENT DAN VECTOR QUANTIZATION (Studi Kasus : Layanan Hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta)." Telematika 17, no. 2 (2020): 68. http://dx.doi.org/10.31315/telematika.v1i1.3380.

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Layanan hotline Universitas Pembangunan Nasional “Veteran” Yogyakarta merupakan layanan yang dapat digunakan oleh semua orang. Layanan tersebut digunakan dosen dan pegawai untuk berbagi informasi dengan bagian-bagian yang berlokasi di gedung rektorat. Penelepon dapat berkomunikasi dengan bagian yang dituju apabila telah teridentifikasi oleh petugas layanan hotline. Terminologi identitas yang terdiri dari nama, jabatan serta asal jurusan atau bagian ditanyakan saat proses identifikasi. Tidak terdapat catatan hasil identifikasi penelepon baik dalam bentuk fisik maupun basis data yang terekam pad

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22

Raghuramireddy, D., and R. Unbehauen. "The two-dimensional differential cepstrum." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 5 (1985): 1335–37. http://dx.doi.org/10.1109/tassp.1985.1164677.

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23

Martin, R. J. "Autoregression and cepstrum-domain filtering." Signal Processing 76, no. 1 (1999): 93–97. http://dx.doi.org/10.1016/s0165-1684(98)00249-7.

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24

Koike, Yasuo. "Cepstrum analysis of pathologic voices." Journal of Phonetics 14, no. 3-4 (1986): 501–7. http://dx.doi.org/10.1016/s0095-4470(19)30698-9.

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25

Aatola, S., and R. Leskinen. "Cepstrum Analysis Predicts Gearbox Failure." Noise Control Engineering Journal 34, no. 2 (1990): 53. http://dx.doi.org/10.3397/1.2827757.

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26

Varma, V. Sai Nitin, and Abdul Majeed K.K. "Advancements in Speaker Recognition: Exploring Mel Frequency Cepstral Coefficients (MFCC) for Enhanced Performance in Speaker Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 88–98. http://dx.doi.org/10.22214/ijraset.2023.55124.

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Abstract: Speaker recognition, a fundamental capability of software or hardware systems, involves receiving speech signals, identifying the speaker present in the speech signal, and subsequently recognizing the speaker for future interactions. This process emulates the cognitive task performed by the human brain. At its core, speaker recognition begins with speech as the input to the system. Various techniques have been developed for speech recognition, including Mel frequency cepstral coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral coefficients (LPCC), Li

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27

de Souza, Edson Florentino, Túlio Nogueira Bittencourt, Diogo Ribeiro, and Hermes Carvalho. "Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges." Sustainability 14, no. 20 (2022): 13290. http://dx.doi.org/10.3390/su142013290.

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In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in

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28

Cheng, Hao, Furui Wang, Linsheng Huo, and Gangbing Song. "Detection of sand deposition in pipeline using percussion, voice recognition, and support vector machine." Structural Health Monitoring 19, no. 6 (2020): 2075–90. http://dx.doi.org/10.1177/1475921720918890.

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Deposits prevention and removal in pipeline has great importance to ensure pipeline operation. Selecting a suitable removal time based on the composition and mass of the deposits not only reduces cost but also improves efficiency. In this article, we develop a new non-destructive approach using the percussion method and voice recognition with support vector machine to detect the sandy deposits in the steel pipeline. Particularly, as the mass of sandy deposits in the pipeline changes, the impact-induced sound signals will be different. A commonly used voice recognition feature, Mel-Frequency Ce

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29

Iksan, Nur, Jaka Sembiring, Nanang Hariyanto, and Suhono Harso Supangkat. "Residential load event detection in NILM using robust cepstrum smoothing based method." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 742. http://dx.doi.org/10.11591/ijece.v9i2.pp742-752.

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Event detection has an important role in detecting the switching of the state of the appliance in the residential environment. This paper proposed a robust smoothing method for cepstrum estimation using double smoothing i.e. the cepstrum smoothing and local linear regression method. The main problem is to reduce the variance of the home appliance peak signal. In the first step, the cepstrum smoothing method removed the unnecessary quefrency by applying a rectangular window to the cepstrum of the current signal. In the next step, the local regression smoothing weighted data points to be smoothe

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30

Gong, Zhao-yu, and Gui-cang Zhang. "Improvement of Point Spread Function Estimation Method for Motion Blurred Image and Image Restoration." Journal of Mathematics and Informatics 24 (2023): 89–98. http://dx.doi.org/10.22457/jmi.v24a08224.

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The estimation of point spread function (PSF) of motion-blurred images is the key to image restoration, which is mainly affected by two physical quantities: blur angle and blur length. The traditional point spread function estimation method is easy to be disturbed by the bright cross in the centre of the blurred image spectrum when estimating the fuzzy angle, which affects the quality of image restoration. In addition, when using the traditional Radon transform to detect fuzzy angles, the area of bright spots in the picture is large, which will cause large fluctuations in the detection results

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31

Iksan, Nur, Jaka Sembiring, Nanang Hariyanto, and Suhono Harso Supangkat. "Residential load event detection in NILM using robust cepstrum smoothing based method." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 742–52. https://doi.org/10.11591/ijece.v9i2.pp742-752.

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Event detection has an important role in detecting the switching of the state of the appliance in the residential environment. This paper proposed a robust smoothing method for cepstrum estimation using double smoothing i.e. the cepstrum smoothing and local linear regression method. The main problem is to reduce the variance of the home appliance peak signal. In the first step, the cepstrum smoothing method removed the unnecessary quefrency by applying a rectangular window to the cepstrum of the current signal. In the next step, the local regression smoothing weighted data points to be smoothe

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32

Dai, Shi Chao, Yan Gao, and Yu Guo. "Rolling Element Bearing Diagnosis Based on Signal Reconstruction from Edited Cepstrum." Advanced Materials Research 915-916 (April 2014): 1221–24. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1221.

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A feature extraction scheme for rolling element bearing (REB) fault diagnosis by editing the cepstrum of the original vibration is introduced in this paper. In the presented approach, the order analysis technology is utilized to convert an even-time-spaced scaled signal to an even-angle-spaced signal by resampling the acquired signal. The discrete lines belonging to gears are removed by editing the cepstrum. Then, the signal is reconstructed from the edited cepstrum. Lastly, clear characteristic frequencies related with the faulty REB can be obtained by the envelope spectrum analysis. Simulati

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33

Shim, JaeSeok, GeoYoung Kim, ByungJin Cho, and JeongSeo Koo. "Application of Vibration Signal Processing Methods to Detect and Diagnose Wheel Flats in Railway Vehicles." Applied Sciences 11, no. 5 (2021): 2151. http://dx.doi.org/10.3390/app11052151.

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This paper studied two useful vibration signal processing methods for detection and diagnosis of wheel flats. First, the cepstrum analysis method combined with order analysis was applied to the vibration signal to detect periodic responses in the spectrum for a rotating body such as a wheel. In the case of railway vehicles, changes in speed occur while driving. Thus, it is difficult to effectively evaluate the flat signal of the wheel because the time cycle of the flat signal changes frequently. Thus, the order analysis was combined with the existing cepstrum analysis method to consider the ch

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34

HU, Feng-song, and Xuan ZHANG. "Speaker recognition method based on Mel frequency cepstrum coefficient and inverted Mel frequency cepstrum coefficient." Journal of Computer Applications 32, no. 9 (2013): 2542–44. http://dx.doi.org/10.3724/sp.j.1087.2012.02542.

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35

Lu, Runyu, Jérôme Antoni, Robert B. Randall, Pietro Borghesani, Wade A. Smith, and Zhongxiao Peng. "Cepstral operational modal analysis for multiple-input systems based on the real cyclic cepstrum." Mechanical Systems and Signal Processing 218 (September 2024): 111578. http://dx.doi.org/10.1016/j.ymssp.2024.111578.

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36

Zhu, Pei Xin, Guo Yong Jin, Yu Quan Yan, and Si Yang Gao. "Fault Diagnosis of Rolling Bearing Based on Improved Independent Component Analysis and Cepstrum Theory." Advanced Materials Research 823 (October 2013): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amr.823.188.

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Based on the advantages of independent component analysis (ICA) and cepstrum, this paper adopts a novel feature extraction scheme for rolling bearing fault diagnosis utilizing improved independent component analysis and cepstrum analysis. Firstly, the fast fixed-point algorithm (FastICA) based on negative entropy was used here as the ICA approach to separate the mixed observation signals of rolling bearing vibration. Then, the largest spectral kurtosis value was used to confirm the characteristic separated signal associated with the Rolling bearing faults. Finally, cepstrum analysis was employ

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37

Tohyama, Mikio, Richard H. Lyon, and Tsunehiko Koike. "Reverberant transfer functions and cepstrum dereverberation." Journal of the Acoustical Society of America 92, no. 4 (1992): 2315. http://dx.doi.org/10.1121/1.405067.

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38

Lauwers, Oliver, Oscar Mauricio Agudelo, and Bart De Moor. "A Multiple-Input Multiple-Output Cepstrum." IEEE Control Systems Letters 2, no. 2 (2018): 272–77. http://dx.doi.org/10.1109/lcsys.2018.2828992.

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39

Fahmy, Mahmoud N. "Measurement of aliasing in cepstrum analysis." Journal of the Acoustical Society of America 78, S1 (1985): S80. http://dx.doi.org/10.1121/1.2023011.

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40

Fangming Wang and P. Yip. "Cepstrum analysis using discrete trigonometric transforms." IEEE Transactions on Signal Processing 39, no. 2 (1991): 538–41. http://dx.doi.org/10.1109/78.80852.

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41

Khare, A., and T. Yoshikawa. "Moment of cepstrum and its applications." IEEE Transactions on Signal Processing 40, no. 11 (1992): 2692–702. http://dx.doi.org/10.1109/78.165656.

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42

Marvi, H., and E. Chilton. "Modified two-dimensional root cepstrum analysis." Electronics Letters 41, no. 5 (2005): 285. http://dx.doi.org/10.1049/el:20047943.

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43

Bonnardot, F. "Influence of speed fluctuation on cepstrum." Mechanical Systems and Signal Processing 119 (March 2019): 81–99. http://dx.doi.org/10.1016/j.ymssp.2018.09.010.

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44

Subramaniam, S., A. P. Petropulu, and C. Wendt. "Cepstrum-based deconvolution for speech dereverberation." IEEE Transactions on Speech and Audio Processing 4, no. 5 (1996): 392–96. http://dx.doi.org/10.1109/89.536934.

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45

Cappe, O., and E. Moulines. "Regularization techniques for discrete cepstrum estimation." IEEE Signal Processing Letters 3, no. 4 (1996): 100–102. http://dx.doi.org/10.1109/97.489060.

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46

Wei, Fu-sheng, and Ming Li. "Cepstrum analysis of seismic source characteristics." Acta Seismologica Sinica 16, no. 1 (2003): 50–58. http://dx.doi.org/10.1007/s11589-003-0006-9.

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47

Kuc, Roman, Kiafar Haghkerdar, and Matt O'Donnell. "Presence of Cepstral Peak in Random Reflected Ultrasound Signals." Ultrasonic Imaging 8, no. 3 (1986): 196–212. http://dx.doi.org/10.1177/016173468600800304.

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A peak in the power cepstrum, or the squared magnitude of the Fourier transform of the data log-power spectrum, is commonly observed when processing reflections from plate-like structures, such as membranes. In this case, the cepstral peak at the smallest nonzero time lag, or quefrency, value can be used to determine the thickness of the plate. For reflections from a medium composed of randomly distributed scatterers, such as liver tissue, a cepstral peak is also commonly observed, but cannot be so intuitively explained as in the deterministic case above. In this paper, it is demonstrated that

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48

Chen, Yan, and Bukhari Martinuzzi. "Machine Learning for Predictive Analytics in the Improvement of English Speech Feature Recognition." Mobile Information Systems 2022 (August 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/3541667.

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The use of deep learning to improve English speaking has seen tremendous development in recent years. This study evaluates the noise that is present in the English speech environment, employs a two-way search method to select the optimum feature set, and applies a quick correlation filter to remove redundant features in order to increase the accuracy of English voice feature identification. In addition, this article designs a low-pass filter in the complex cepstrum domain to filter the room impulse response in order to obtain the estimated value of the complex cepstrum of the original speech s

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49

Xu, Hong Bo, Guo Hua Chen, Xin Hua Wang, and Jun Liang. "The Application of EMD and ARMA Bi-Cepstrum Fault Diagnosis Method in Gearbox of Overhead Traveling Crane." Advanced Materials Research 308-310 (August 2011): 88–91. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.88.

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Abstract:

For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

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Pidl, Renáta. "Application of a Novel Methodology for the Detection of Harmonic Vibration in the Acceleration-Time Functions of Vehicle Platforms." Strojnícky časopis - Journal of Mechanical Engineering 72, no. 2 (2022): 139–48. http://dx.doi.org/10.2478/scjme-2022-0024.

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Abstract:

Abstract In this paper, an attempt is made to determine whether Cepstrum analysis can be applied to detect pure harmonic sine waveform acceleration-time signals in a frequency sub-band from the acceleration-time function of vehicle load platforms. Based on Cepstrum evaluation of specific measurement results at different speeds and under different road conditions, the analysis reveals that no frequency band exhibits full-period pure sinusoidal excitation, which casts doubt regarding usuage of sinusoidal sliding frequency tests for package shaking tests.

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