P5770Importance of temporal stability in frequency analysis for persistent atrial fibrillation: fast fourier transform analysis versus continuous wavelet transform analysis

This paper is concerned with a synthesis study of the fast Fourier transform (FFT) and the continuous wavelet transform (CWT) in analysing the phonocardiogram signal (PCG). It is shown that the continuous wavelet transform provides enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency PCG signal characteristics and consequently aid to diagnosis. Similary, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties.

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Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform

International Journal of Advances in Intelligent Informatics ◽

10.26555/ijain.v3i3.114 ◽

2017 ◽

Vol 3 (3) ◽

pp. 146

Author(s):

Yovinia Carmeneja Hoar Siki ◽

Natalia Magdalena Rafu Mamulak

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Frequency Analysis ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Short Time Fourier Transform ◽

Time Frequency Analysis ◽

Time Frequency ◽

Music Signal ◽

Short Time

Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.

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P01-Comparison of short-time Fourier transform and continuous wavelet transform for frequency analysis of sleep EEG

Clinical Neurophysiology ◽

10.1016/j.clinph.2018.01.046 ◽

2018 ◽

Vol 129 (4) ◽

pp. e14

Author(s):

V. Gerla ◽

E. Saifutdinova ◽

M. Macas ◽

A. Mladek ◽

L. Lhotska

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Frequency Analysis ◽

Continuous Wavelet Transform ◽

Sleep Eeg ◽

Continuous Wavelet ◽

Short Time Fourier Transform ◽

Short Time

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An Adaptive Time–Frequency Representation and its Fast Implementation

Journal of Vibration and Acoustics ◽

10.1115/1.2424969 ◽

2006 ◽

Vol 129 (2) ◽

pp. 169-178 ◽

Cited By ~ 5

Author(s):

Bao Liu ◽

Sherman Riemenschneider ◽

Zuowei Shen

Keyword(s):

Fourier Transform ◽

Wavelet Transform ◽

Frequency Analysis ◽

Continuous Wavelet Transform ◽

Continuous Wavelet ◽

Short Time Fourier Transform ◽

Time Frequency ◽

Frequency Representation ◽

Adaptive Time ◽

Short Time

This paper presents a fast adaptive time–frequency analysis method for dealing with the signals consisting of stationary components and transients, which are encountered very often in practice. It is developed based on the short-time Fourier transform but the window bandwidth varies along frequency adaptively. The method therefore behaves more like an adaptive continuous wavelet transform. We use B-splines as the window functions, which have near optimal time–frequency localization, and derive a fast algorithm for adaptive time–frequency representation. The method is applied to the analysis of vibration signals collected from rotating machines with incipient localized defects. The results show that it performs obviously better than the short-time Fourier transform, continuous wavelet transform, and several other most studied time–frequency analysis techniques for the given task.

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