Level quantization effects in digital signal processing by discrete Fourier transform method

2010 ◽  
Author(s):  
G. S. Khanyan
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Digital Signal Processing ◽  
Discrete Fourier Transform ◽  
Digital Signal ◽  
Transform Method ◽  
Fourier Transform Method ◽  
Level Quantization

scholarly journals Digital signal processing in telecommunications based on parametric discrete Fourier transform

2019 ◽  
Vol 30 ◽  
pp. 04010 ◽  
Author(s):  
Olga Ponomareva ◽  
Alexey Ponomarev ◽  
Natalya Smirnova
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Digital Signal Processing ◽  
Discrete Fourier Transform ◽  
Digital Signal ◽  
Discrete Transformation ◽  
Stochastic Properties

A generalization of the discrete Fourier transform in the form of a parametric discrete Fourier transform is proposed. The analytical and stochastic properties of the introduced discrete transformation are investigated. An example of the application of the parametric discrete Fourier transform in telecommunications is given - a generalization of the well-known Herzel algorithm

scholarly journals Comparison of Discrete Fourier Transform and Fast Fourier Transform with Reduced Number of Multiplication and Addition Operations

2020 ◽  
Vol Volume 14 ◽  
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Digital Signal Processing ◽  
Fast Fourier Transform ◽  
Discrete Fourier Transform ◽  
Symmetry Property ◽  
Digital Signal ◽  
Basic Structure ◽  
Phase Factor ◽  
Normal Order

Fast Fourier Transform is an advanced algorithm for computing Discrete Fourier Transform efficiently. Although the results available from the operation of Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) are same, but exploiting the periodicity and symmetry property of phase factor Fast Fourier Transform computes the Discrete Fourier Transform using reduced number of multiplication and addition operations. The basic structure used in the operations of Fast Fourier Transform is the Butterfly structure. For the implementation of Fast Fourier Transform the two methods are used such as decimation in time (DIT) and decimation in frequency (DIF). Both the methods give same result but for decimation in time of Fast Fourier Transform bit reversed inputs are applied and for decimation in frequency of Fast Fourier Transform normal order inputs are applied, and the result is reversed again. In this paper, operations for DFT and FFT have been discussed and shown with examples. It is found that generalized formula for FFT have been described same in the books, but the expressions in the intermediate computations for the first decimation and second decimation are different in the various books of Digital Signal Processing. The expressions in the intermediate computation of FFT described in different books are broadly compared in this paper

Digital Signal Processing for Step-Scan Fourier Transform Infrared Photoacoustic Spectroscopy

1997 ◽  
Vol 51 (4) ◽  
pp. 453-460 ◽  
Author(s):  
David L. Drapcho ◽  
Raul Curbelo ◽  
Eric Y. Jiang ◽  
Richard A. Crocombe ◽  
William J. McCarthy
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Digital Signal Processing ◽  
Phase Modulation ◽  
Modulation Frequency ◽  
Digital Signal ◽  
Sampling Depth ◽  
Ft Ir ◽  
Infrared Photoacoustic Spectroscopy ◽  
Additional Hardware

A software-based digital signal processing (DSP) method using the data system processor has been developed to demodulate the photoacoustic responses of a sample to the fundamental phase modulation frequency and its harmonic frequencies (up to the ninth harmonic) in step-scan FT-IR photoacoustic measurements, without the use of any additional hardware. The DSP algorithm and its sampling depth multiplexing advantages are compared to conventional hardware demodulation. Comparison of results from the DSP method to those from hardware demodulators are shown at both the phase modulation frequency and the harmonics, and application of the DSP method to step-scan photoacoustic measurements with phase modulation is discussed as it applies to obtaining depth profile information in heterogeneous materials.

Classification of Women's Voices Using Fast Fourier Transform (FFT) Method

2021 ◽  
Vol 10 (1) ◽  
pp. 59
Author(s):  
Made Sri Ayu Apsari ◽  
I Made Widiartha
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Digital Signal Processing ◽  
Fast Fourier Transform ◽  
Digital Signal ◽  
Women's Voices ◽  
Mezzo Soprano

Everyone has a different kind of voice. Based on gender, voice type is divided into six parts, namely soprano, mezzo soprano, and alto for women; and tenor, baritone, and bass in men. Each type of sound has a different range and with different frequencies. This study classified the type of voice in women using the Fast Fourier Transform (FFT) method by recording the voices of each user which would then be processed using the FFT method to obtain the appropriate sound range. This research got results with an accuracy of up to 80%.The results obtained from this study are quite appropriate and it is proven that the FFT method can be used in digital signal processing.

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