Wavelet-based cepstrum calculation

Sanchez, Fabrício Lopes; Barbon, Júnior Sylvio; Vieira, Lucimar Sasso; Guido, Rodrigo Capobianco; Fonseca, Everthon Silva; Scalassara, Paulo Rogério; Maciel, Carlos Dias; Pereira, José Carlos; Chen, Shi-Huang

doi:10.1016/j.cam.2008.03.016

Cited by 8 publications

(10 citation statements)

References 3 publications

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“…Symmlets or coiflets are found to be the best choice of wavelet function for the wavelet‐based cepstrum (Sanchez et al . ). Note that the wavelet‐based transform does not require an inverse DWPT, but it does require the length of the calculated signal to be a power of 2.…”

Section: Principles and Methodsmentioning

confidence: 97%

“…In each level of decomposition, the result of the DWPT should alternate the order of the pair of filters to form an NFO (Sanchez et al . ). Let

k = \sum_{i = 0}^{n - 1} k_{i} 2^{i}

be the order of the DWT in binary representation, and

g (k)

be the Gray code corresponding to k .…”

Section: Principles and Methodsmentioning

confidence: 97%

“…The wavelet‐based cepstrum produced by Sanchez et al . () is the discrete wavelet packet transform (DWPT) of the real natural logarithm of the magnitude of the DWPT of a signal shown by the following equation:

C_{w} (q) = D W P T \{{prefixlog}_{10} ((||, D W P T ({}, s (t))))\},

where

D W P T (•)

represents the discrete wavelet packet transform. The wavelet‐based cepstrum filters the input signal, scales it using the logarithmic function, filters it again, and then calculates the energy of each sub‐band.…”

Section: Principles and Methodsmentioning

confidence: 99%

“…Recently, Sanchez et al . () presented a wavelet‐based cepstrum for pitch extraction in the automatic speech and speaker recognition systems, whose core operation uses wavelet packet transform (WPT) instead of Fourier transform and found that the wavelet‐based cepstrum has better effectiveness and accuracy than the traditional cepstrum based on Fourier transform. In this paper, we modify the wavelet‐based cepstrum for more suitably assessing seismic data, and a novel approach for spectral decomposition with a wavelet‐based cepstrum is introduced.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet‐based cepstrum decomposition of seismic data and its application in hydrocarbon detection

Xue

Cao

Tian

et al. 2015

Geophysical Prospecting

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A B S T R A C THow to use cepstrum analysis for reservoir characterization and hydrocarbon detection is an initial question of great interest to exploration seismologists. In this paper, wavelet-based cepstrum decomposition is proposed as a valid technology for enhancing geophysical responses in specific frequency bands, in the same way as traditional spectrum decomposition methods do. The calculation of wavelet-based cepstrum decomposition, which decomposes the original seismic volume into a series of common quefrency volumes, employs a sliding window to move over each seismic trace sample by sample. The key factor in wavelet-based cepstrum decomposition is the selection of the sliding-window length as it limits the frequency ranges of the common quefrency section. Comparison of the wavelet-based cepstrum decomposition with traditional spectrum decomposition methods, such as short-time Fourier transform and wavelet transform, is conducted to demonstrate the effectiveness of the wavelet-based cepstrum decomposition and the relation between these two technologies. In hydrocarbon detection, seismic amplitude anomalies are detected using wavelet-based cepstrum decomposition by utilizing the first and second common quefrency sections. This reduces the burden of needing dozens of seismic volumes to represent the response to different mono-frequency sections in the interpretation of spectrum decomposition in conventional spectrum decomposition methods. The model test and the application of real data acquired from the Sulige gas field in the Ordos Basin, China, confirm the effectiveness of the seismic amplitude anomaly section using wavelet-based cepstrum decomposition for discerning the strong amplitude anomalies at a particular quefrency buried in the broadband seismic response. Wavelet-based cepstrum decomposition provides a new method for measuring the instantaneous cepstrum properties of a reservoir and offers a new field of processing and interpretation of seismic reflection data.

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Section: Principles and Methodsmentioning

confidence: 97%

“…In each level of decomposition, the result of the DWPT should alternate the order of the pair of filters to form an NFO (Sanchez et al . ). Let

k = \sum_{i = 0}^{n - 1} k_{i} 2^{i}

be the order of the DWT in binary representation, and

g (k)

be the Gray code corresponding to k .…”

Section: Principles and Methodsmentioning

confidence: 97%

C_{w} (q) = D W P T \{{prefixlog}_{10} ((||, D W P T ({}, s (t))))\},

where

D W P T (•)

Section: Principles and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wavelet‐based cepstrum decomposition of seismic data and its application in hydrocarbon detection

Xue

Cao

Tian

et al. 2015

Geophysical Prospecting

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“…A escala Mel, fundamentada em um modelo aprimorado para frequências de voz referente ao padrão perceptual humano, serve como base para avaliação do cepstrum. De acordo com (3), a análise cepstral tem sido largamente empregada em processamento digital de sinais de voz, e a implementação mais popularé aquela que combina a escala cepstrum com uma dilatação não-linear, de acordo com a escala Mel (38)(39). Assim, os Coeficientes Cepstrais na Escala Mel (MFCCs -Mel Frequency cepstral coefficients), são uma representação dos componentes espectrais do sinal, de maneira mais conveniente e particularizada para um sinal de voz.…”

Section: Coeficientes Cepstrais Na Escala Mel (Mfccs)unclassified

Identificação de patologias na laringe com base na >i<Discriminative Paraconsistent Machine>/i< (DPM)

Júnior¹

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Este trabalho de doutorado apresenta duas inovações: a Discriminative Paraconsistent Machine (DPM), que consiste em um novo classificador elaborado com base na lógica paraconsistente anotada (LPA) e a aplicação da DPM para a identificação de patologias na laringe, por meio de exames nos sinais de voz de um locutor. Não há relatos na literatura sobre o uso da LPA para construção de um classificador e sobre suas aplicações para a finalidade proposta. Os resultados obtidos são motivadores, indicando um avanço naárea. Palavras-chave: Processamento de sinais. Patologias da laringe. Inteligência artificial.

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