The compressive stress signal of soil during vibration compaction is an unstable and transient saltation signal accompanied by broadband noise, and the spectra of the signal and noise always overlap. To extract the ideal original signal from noisy data, this paper studies several signal de-noising methods such as low-pass filtering, multi-resolution wavelet transform, spectrum subtraction and independent component analysis. Experiments show that the traditional low-pass filter is only applicable when the spectra of the signal and noise can be separated in the frequency domain. The multi-resolution wavelet transform can decompose the signal into different frequency bands and remove the noise efficiently by extracting useful the frequency band of the signal, but this method is not reliable when the signal to noise ratio (SNR) is low. Spectrum subtraction can remove strong background noise with stationary statistical characteristics even if the noise level is high and the spectrum of the signal overlaps with that of the noise. Independent component analysis can extract weak signals which are combined with heavy noise and can separate the noise from signal effectively when the independent channel hypothesis holds. These de-noising methods are of great importance for further analysing vibration signals in engineering.