# What technique could I use to smooth the right amount these signals?

I'm developing an application which one of its capabilities is peak detection. I expect to handle three types of the similar data (Acceleration vs Frequency) but measured in different laboratories and with different sampling and noise. The data is not equally spaced in frequency.

Here is the plot of each of them:

Detail of the previous one

The first contains about 2000 points and there are a lot of local maxima detected. I can't use an average-smooth because the last one contains only 100 points and if I do so I will be missing important peaks (each peak occupy only 3-7 points).

What can I do to improve the detection of peaks and how it could be done?

Thank you very much.

• Wavelets are very useful for this application. – Mahdi Khosravi Aug 21 '13 at 12:10

A local maximum at $X(f)$ is considered a peak of interest if for some chosen small value $\epsilon$ and some chosen height $\mu$ , we can satisfy the following
$$\exists \, \delta_1 , \delta_2 \lt \epsilon \quad s.t. \quad X(f)-X(f-\delta_1) > \mu \quad \text{and} \quad X(f)-X(f+\delta_2) > \mu$$