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About 10,000 papers for “Wavelet transform”

Wavelet Transform

Yunlong Sheng · ˜The œelectrical engineering handbook/Electrical engineering handbook series/Electrical engineering handbook · 1998

Cited by 1

Wavelet Transform

Yulong Sheng · ˜The œelectrical engineering handbook/Electrical engineering handbook series/Electrical engineering handbook · 2010

Cited by 2

Wavelet Transform

Neep Hazarika, Jean Zhu, Chen Ah et al. · 2014

Ahsrr-ucr-This paper describes the application of an artificial neural network (ANN) technique together with a feature extraction technique, viz., the wavelet transform, for the classification of EEG signals. Three classes of EEG signals were used: Normal, Schizophrenia (SCH), and Obsessive Compulsive Disorder (OCD). The architecture of the artificial neural network used in the classification is a three-layered feedforward network which implements the backpropagation of error learning algorithm. After training, the network with wavelet coefficients was able to correctly classify over 66 % of the normal class and 71 % of the schizophrenia class of EEG’s. The wavelet transform thus provides a potentially powerful technique for preprocessing EEG signals prior to classification. Kejwords- EEG Classification, Neural Networks,

Cited by 0

Wavelet transform

Boris Valter · Digital Repository (National Repository of Grey Literature) · 2016

Wavelet transform is a term from signal analysis. It is mostly used in physics, but also in finance, where we can use it to find a trend in different financial data. In the first chapter we will describe two older methods of signal analysis: Fourier transform and short-time Fourier transform. In the second chapter we show, how wavelet transform works, derive frequently used algorithm for calculating discrete wavelet transform and at the end we show several practical examples. This thesis was considered to deepen the knowledge of time-frequency analysis of signals, for better understanding of the principle, how wavelet transform works, and for potential extending its use. Powered by TCPDF (www.tcpdf.org)

Cited by 0

Wavelet transform

Sachin P. Nanavati, Prasanta K. Panigrahi · Resonance · 2004

Cited by 27

Wavelet transform

Boris Valter · Digital Repository (National Repository of Grey Literature) · 2016

Wavelet transform is a term from signal analysis. It is mostly used in physics, but also in finance, where we can use it to find a trend in different financial data. In the first chapter we will describe two older methods of signal analysis: Fourier transform and short-time Fourier transform. In the second chapter we show, how wavelet transform works, derive frequently used algorithm for calculating discrete wavelet transform and at the end we show several practical examples. This thesis was considered to deepen the knowledge of time-frequency analysis of signals, for better understanding of the principle, how wavelet transform works, and for potential extending its use. Powered by TCPDF (www.tcpdf.org)

Cited by 0

Wavelet Transform

Bingqi Chen, Siyao Chen · Advanced and intelligent manufacturing in China · 2026

Cited by 0

Wavelet transform.

Emad S. Hassan (17775798), Badawi Neyazi (17965659), H. S. Seddeq (17965662) et al. · Figshare · 2024

<div><p>This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.</p></div>

Cited by 0Open access

The Wavelet Packet Transform 1 Extending the wavelet transform

Mac A. Cody · 2014

The wavelet transform enables analysis of data at multiple levels of resolution (also known as "scale"). In addition, transient events in the data are preserved by the analysis. When the wavelet transform (WT) is applied to a signal in the time domain, the result is a two-dimensional, time-scale domain analysis of the signal. The transform has proven useful for the compression and analysis of signals and images. The fast wavelet transform (FWT) is an ecient implementation of the discrete wavelet transform (DWT). The DWT is the WT as applied to a regularly sampled data sequence. The transform of the data exhibits discrete steps in time on one axis, and discrete steps of resolution on the another. The algorithm and a C language implementation of the FWT were presented in my article "The Fast Wavelet Transform " (DDJ,

Cited by 0

Interpolating Wavelet Transform

David L. Donoho · 1992

We describe several "wavelet transforms" which characterize smoothness spaces and for which the coefficients are obtained by sampling rather than integration. We use them to re-interpret the empirical wavelet transform, i.e. the common practice of applying pyramid filters to samples of a function.

Cited by 0

Redundant Wavelet Transform

Jean‐Luc Starck, Fionn Murtagh, Jalal Fadili · Cambridge University Press eBooks · 2015

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Cited by 1