Smoothing a noisy signal

Hey everyone,

I need some help smoothing out a noisy signal. I know of one or two methods but I am not sure what the best way of doing it is. Can someone tell me what is the most efficient way of smoothing?

Thanks for the help!

[David Polcari]

Here is my dataset:

voltage_B1 = [1.0110

1.0110

1.0134

1.0134

1.0122

1.0122

1.0122

1.0134

1.0122

1.0134

1.0122

1.0134

1.0098

1.0134

1.0122

1.0134

1.0122

1.0110

1.0122

1.0134

1.0122

1.0122

1.0122

1.0134

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0134

1.0122

1.0110

1.0110

1.0110

1.0122

1.0134

1.0134

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0134

1.0147

1.0122

1.0122

1.0122

1.0122

1.0134

1.0110

1.0122

1.0122

1.0122

1.0122

1.0122

1.0147

1.0134

1.0134

1.0147

1.0134

1.0134

1.0147

1.0147

1.0134

1.0134

1.0134

1.0134

1.0134

1.0122

1.0134

1.0147

1.0147

1.0159

1.0134

1.0134

1.0147

1.0134

1.0147

1.0134

1.0147

1.0122

1.0134

1.0147

1.0159

1.0159

1.0147

1.0147

1.0147

1.0134

1.0159

1.0147

1.0134

1.0134

1.0122

1.0122

1.0122

1.0134

1.0134

1.0134

1.0147

1.0122

1.0134

1.0159

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0098

1.0134

1.0122

1.0098

1.0122

1.0122

1.0098

1.0110

1.0122

1.0122

1.0098

1.0122

1.0110

1.0122

1.0122

1.0122

1.0098

1.0134

1.0110

1.0122

1.0110

1.0122

1.0122

1.0098

1.0110

1.0122

1.0110

1.0122

1.0134

1.0122

1.0122

1.0110

1.0122

1.0110

1.0122

1.0122

1.0134

1.0159

1.0110

1.0110

1.0134

1.0134

1.0122

1.0122

1.0122

1.0134

1.0122

1.0134

1.0122

1.0134

1.0098

1.0134

1.0122

1.0134

1.0122

1.0110

1.0122

1.0134

1.0122

1.0122

1.0122

1.0134

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0134

1.0122

1.0110

1.0110

1.0110

1.0122

1.0134

1.0134

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0134

1.0147

1.0122

1.0122

1.0122

1.0122

1.0134

1.0110

1.0122

1.0122

1.0122

1.0122

1.0122

1.0147

1.0134

1.0134

1.0147

1.0134

1.0134

1.0147

1.0147

1.0134

1.0134

1.0134

1.0134

1.0134

1.0122

1.0134

1.0147

1.0147

1.0159

1.0134

1.0134

1.0147

1.0134

1.0147

1.0134

1.0147

1.0122

1.0134

1.0147

1.0159

1.0159

1.0147

1.0147

1.0147

1.0134

1.0159

1.0147

1.0134

1.0134

1.0122

1.0122

1.0122

1.0134

1.0134

1.0134

1.0147

1.0122

1.0134

1.0159

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0098

1.0134

1.0122

1.0098

1.0122

1.0122

1.0098

1.0110

1.0122

1.0122

1.0098

1.0122

1.0110

1.0122

1.0122

1.0122

1.0098

1.0134

1.0110

1.0122

1.0110

1.0122

1.0122

1.0098

1.0110

1.0122

1.0110

1.0122

1.0134

1.0122

1.0122

1.0110

1.0122

1.0110

1.0122

1.0122

1.0134

1.0159

1.0122

1.0122

1.0122

1.0134

1.0134

1.0134

1.0134

1.0134

1.0147

1.0147

1.0134

1.0147

1.0147

1.0122

1.0122

1.0122

1.0122

1.0122

1.0122

1.0147

1.0134

1.0122

1.0147

1.0147

1.0134

1.0122

1.0134

1.0122

1.0147

1.0134

1.0134

1.0122

1.0134

1.0122

1.0134

1.0134

1.0122

1.0134

1.0134

1.0147

1.0134

1.0122

1.0122

1.0134

1.0147

1.0122

1.0134

1.0122

1.0134

1.0134

1.0134

1.0122

1.0134

1.0122

1.0122

1.0122

1.0134

1.0122

1.0134

1.0171

1.0134

1.0122

1.0110

1.0122

1.0122

1.0122

1.0134

1.0134

1.0134

1.0134

1.0122

1.0134

1.0134

1.0122

1.0122

1.0134

1.0122

1.0134

1.0134

1.0134

1.0134

1.0134

1.0110

1.0147

1.0159

1.0122

1.0134

1.0122

1.0159

1.0147

1.0147

1.0147

1.0159

1.0147

1.0134

1.0134

1.0159

1.0122

1.0122

1.0147

1.0110

1.0134

1.0147

1.0134

1.0134

1.0134

1.0147

1.0134

1.0159

1.0122

1.0147

1.0134

1.0147

1.0147

1.0134

1.0134

1.0147

1.0122

1.0159

1.0159

1.0147

1.0134

1.0147

1.0147

1.0147

1.0147

1.0159

1.0147

1.0147

1.0147

1.0147

1.0159

1.0147

1.0159

1.0159

1.0159

1.0147

1.0159

1.0159

1.0159

1.0147

1.0159

1.0159

1.0171

1.0171

1.0134

1.0159

1.0159

1.0159

1.0134

1.0159

1.0159

1.0159

1.0147

1.0159

1.0147

1.0159

1.0159

1.0159

1.0159

1.0159

1.0171

1.0171

1.0159

1.0171

1.0171

1.0171

1.0159

1.0159

1.0147

1.0159

1.0134

1.0171

1.0159

1.0159

1.0171

1.0159

1.0171

1.0171

1.0147

1.0171

1.0147

1.0171

1.0159

1.0183

1.0195

1.0171

1.0183

1.0134

1.0171

1.0134

1.0122

1.0134

1.0122

1.0122

1.0122

1.0134

1.0122

1.0134

1.0171

1.0134

1.0122

1.0110

1.0122

1.0122

1.0122

1.0134

1.0134

1.0134

1.0134

1.0122

1.0134

1.0134

1.0122

1.0122

1.0134

1.0122

1.0134

1.0134

1.0134

1.0134

1.0134

1.0110

1.0147

1.0159

1.0122

1.0134

1.0122

1.0159

1.0147

1.0147

1.0147

1.0159

1.0147

1.0134

1.0134

1.0159

1.0122

1.0122

1.0147];

Answer 1

[Walter Roberson]

The most efficient way of smoothing is to leave the data untouched.

conv(signal, ones(1,3)/3) is fairly efficient computationally, but you could probably do better if you created a custom mex routine.

If you were instead perhaps wondering about the most effective way of smoothing, then you need to define your goals for the results.

Answer 2

[Sandarva Khanal]

Like Walter said, you need to define your goals for the results. One simple way can be to use a running average of may be about 3 - 5 data from your data set.

For example, if you are interested in finding the running average of 3 data-points from your data set, your 2nd data could be the average of first three data-points. Your 3rd data will be the average of data-point indexed 2 to 4, and so on....remember that in this case, you will not usually change the first data-point.

[Walter Roberson]

conv(signal, ones(1,3)/3) is one method of implementing a 3 point running average.