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Cepstrum

About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors compared the performance of the segmented chirp Z-transform and the cepstrum with a high flexible window for thickness measurements in thin multilayer structures.
Abstract: Digital signal processing techniques set up to detect echoes in noisy environments and to thus carry out thickness measurements in thin multilayer structures are discussed. In particular, the cepstrum and the segmented chirp Z-transform are analyzed and compared, highlighting their performances in relation to noise characteristics. A suitable operating procedure was set up, based on an initial emulation phase in which simulated signals are considered, followed by a second phase in which signals were processed. The results show that optimum performances can be achieved by using the segmented chirp Z-transform together with a high flexible window. >

23 citations

Proceedings ArticleDOI
01 May 1977
TL;DR: The factorization method provides a means for evaluating other methods for computing the complex cepstrum by completely factoring its z-transform and summing the easily obtainedcomplex cepstra of the factors.
Abstract: We compute the complex cepstrum of a finite duration slgnal by completely factoring its z-transform and summing the easily obtained complex cepstra of the factors. The method requires no phase unwrapping and introduces no aliasing. The only approximation is the unavoidable finite time window. Thus the factorization method provides a means for evaluating other methods for computing the complex cepstrum.

22 citations

Patent
31 Mar 2010
TL;DR: In this paper, an SVM-based device for analyzing audio data by using SVM method is presented. But the device is characterized by comprising an input unit, a preprocessing unit, classifying unit and a post processing unit, wherein the input unit is used for inputting audio stream; the preprocessing units are used for preprocessing the audio stream to obtain a characteristic parameter of each frame; the classifying units analyzes the category to which each frame belongs according to the characteristic parameter; and the post processing units carry out post processing on the classification result of the class
Abstract: The invention provides a device for analyzing audio data by using an SVM method. The device is characterized by comprising an input unit, a preprocessing unit, a classifying unit and a post processingunit, wherein the input unit is used for inputting audio stream; the preprocessing unit is used for preprocessing the audio stream to obtain a characteristic parameter of each frame; the classifyingunit analyzes the category to which each frame belongs according to the characteristic parameter; and the post processing unit carries out post processing on the classifying result of the classifyingunit to obtain the final subsection result. The characteristic parameter comprises short time average energy, subband energy, zero-crossing rate, Mel frequency domain cepstrum coefficient, delta Mel frequency domain cepstrum coefficient, spectrum flux and fundamental tone frequency. The invention realizes quick retrieval of splendid contents, and can save the time of audiences and meet the watching demand of the audiences.

22 citations

Proceedings ArticleDOI
01 Mar 2012
TL;DR: The feature extraction technique with maximum identification accuracy and less false acceptance rate is identified by varying initial centroids and the algorithms were compared using TIMIT database of 100 speakers.
Abstract: In this paper, various feature extraction techniques for text independent speaker identification such as Mel-frequency cepstral coefficients(MFCC), Modified Mel-frequency cepstral coefficients(MMFCC), Bark frequency cepstral coefficients(BFCC), Revised Perceptual liner prediction (RPLP) and linear predictive coefficient cepstrum (LPCC) are implemented and the comparison is done based on performance and computation time. For modeling speaker identity vector quantization (VQ) codebook have been used. The feature extraction technique with maximum identification accuracy and less false acceptance rate is identified by varying initial centroids. The algorithms were compared using TIMIT database of 100 speakers.

22 citations

Journal ArticleDOI
TL;DR: An ECG based driver distraction detection system using Mel-frequency cepstrum representation and convolutional neural networks (CNN) and a recipe to extract Mel frequency filter bank coefficients in time and frequency domains is presented.

22 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202386
2022206
202160
202096
2019135
2018130