In digital signal processing, to smooth a signal it is to create a function that attempts to capture important patterns in the signal, while simultaneously attenuating the noise or other rapid changes in the signal. One of the most common algorithms for signal smoothing is the moving average, often used to attempt to capture important trends in the signal. The simplest smoothing algorithm is the rectangular or un-weighted sliding average smooth. This method replaces each point in the signal with the average of several adjacent points, where the number of adjacent points is an odd number otherwise known as the smooth width. MatDeck contains the function movavg() which is used to smooth the signal in a aforementioned way.
The various Smoothing algorithms implemented in MatDeck are:
Butterworth filters
Very common for noise attenuation and signal smoothing, functions:
- buttband()
- butlohi()
Digital filters
Functions which are used to design different digital filters are:
- iirfreqres()
- iirfilter()
- initiirfilter()
- iirphasedelay()
Low pass filters
Functions used to design different digital filters, including low-pass filters which are commonly used for signal smoothing are:
- cheby1lohi()
- cheby2lohi()
- elliplohi()
- firlowpass()
- firfilter()
- firopt()
- firoptord()
- initfirfilter()
Moving average
Function for the rectangular or un-weighted sliding average smooth is:
- movavg()
