2018Bulgarian Portal for Open ScienceOpen access

A Method for Colored Noise Generation

Hristo Zhivomirov

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Abstract

The present paper addresses the generation of power-law, colored digital noise signals (sequences) with arbitrary spectral slope. In the beginning, brief background information is given about some noise features. Further, a newly proposed method is described, based on generation of a white noise signal, its transformation into the frequency domain, spectral processing and inverse transform back into the time domain. Computer simulations are performed to confirm the consistency of the algorithm, including estimation of the power spectral density and the autocorrelation, along with example of its outperformance in comparison with the corresponding in-built Matlab® function.

About this research paper

What this paper is about

The present paper addresses the generation of power-law, colored digital noise signals (sequences) with arbitrary spectral slope. In the beginning, brief background information is given about some noise features. Further, a newly proposed method is described, based on generation of a white noise signal, its transformation into the frequency domain, spectral processing and inverse transform back into the time domain. Computer simulations are performed to confirm the consistency of the algorithm, including estimation of the power spectral density and the autocorrelation, along with example of its outperformance in comparison with the corresponding in-built Matlab® function.

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OpenAlex reports 52 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The present paper addresses the generation of power-law, colored digital noise signals (sequences) with arbitrary spectral slope. In the beginning, brief background information is given about some noise features. Further, a newly proposed method is described, based on generation of a white noise signal, its transformation into the frequency domain, spectral processing and inverse transform back into the time domain. Computer simulations are performed to confirm the consistency of the algorithm, including estimation of the power spectral density and the autocorrelation, along with example of its outperformance in comparison with the corresponding in-built Matlab® function.

Key concepts: Colors of noise, Autocorrelation, Noise (video), Spectral density estimation, White noise, Spectral density, Algorithm, Computer science

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