Noise Analysis with Matlab
The present code is a Matlab program for analysis of noise signals. The analysis includes:
1) Options for:
- signal detrending;
- signal normalization.
2) Plotting of the:
- signal in the time domain (oscillogram);
- signal in the frequency domain (periodogram);
- signal in the time-frequency domain (spectrogram);
- amplitude distribution of the signal (histogram);
- autocorrelation function of the signal (correlogram).
3) Displaying of the:
- minimum and maximum value of the signal;
- mean value (DC-value) and standard deviation (RMS-value);
- skewness (tailness) and kurtosis (peakedness);
- crest-factor CF;
- dynamic range DR;
- autocorrelation time;
- test result for stationarity of the signal.
The code is based on the theory described in:
[1] D. Manolakis, V. Ingle. Applied Digital Signal Processing. Cambridge, Cambridge University Press, 2011.
[2] G. Heinzel, A. Rudiger, R. Schilling. Spectrum and spectral density estimation by the Discrete Fourier transform (DFT), including a comprehensive list of window functions and some new flat-top windows. Germany, Hannover, Max-Planck-Institut für Gravitationsphysik, 2002.
Zitieren als
Hristo Zhivomirov (2024). Noise Analysis with Matlab (https://www.mathworks.com/matlabcentral/fileexchange/71887-noise-analysis-with-matlab), MATLAB Central File Exchange. Abgerufen.
Kompatibilität der MATLAB-Version
Plattform-Kompatibilität
Windows macOS LinuxKategorien
- Signal Processing > Signal Processing Toolbox > Spectral Analysis > Spectral Measurements >
- Engineering > Mechanical Engineering > Acoustics, Noise and Vibration >
Tags
Quellenangaben
Inspiriert von: Sound Analysis with Matlab
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Live Editor erkunden
Erstellen Sie Skripte mit Code, Ausgabe und formatiertem Text in einem einzigen ausführbaren Dokument.
Version | Veröffentlicht | Versionshinweise | |
---|---|---|---|
1.1.0 | A new version of the code has been uploaded. |
||
1.0.0 |