Method: Five sound recordings were made of five dental air turbine handpieces in use with different amounts of loading: no load, 1N, 2N and 2.5N according to BS EN ISO 7785-1. The sound recordings were analysed using FFT power spectral density plots in the Matlab Signal Processing Tool (SPT). A Normalised Least Mean Square (NLMS) adaptive filter algorithm was applied to the recordings. Comparisons were made between the original recordings and the filtered recordings.
Results: Five air turbine handpieces were tested and sound recordings at the four different loads were recorded and analysed. The application of adaptive filtering brought about noise reduction in each case with a maximum noise reduction of the sound peak of 27dB. For each test the noise reduction was always greater than 6dB and was statistically significant (p<0.001) for each handpiece at each loading condition.
Conclusions: Adaptive filtering can be used to specifically reduce the unpleasant noise from dental air turbine handpieces without removing other background sounds. Supported by W&H.