## 5 Conclusions  
This article evaluates and compares the effectiveness of some of the most popular LMS-based algorithms applied to a multichannel ANC system, analyzing their performance in terms of speed of convergence and global attenuation of the disturbance for different kinds of signals. The experimental comparison has allowed us to evaluate some important practical aspects which would not otherwise be observable when using a computer simulation. From the experimental comparison carried out on a small confined space (tractor cabin) in a hemi-anechoic chamber with a sound field generated by a dodecahedron placed outside the cabin, it is possible to observe that the best position for the reference microphone of the \begin{table}
\begin{tabular}{l|c c|c c|c c|c c} \hline \hline \multicolumn{2}{c}{**500 Hz**} & \multicolumn{2}{c}{**FF\_FXLMS**} & \multicolumn{2}{c}{**FB\_FXLMS**} & \multicolumn{2}{c}{**FF\_FURLMS**} & \multicolumn{2}{c}{**FF\_FXNLMS**} \\ \hline Side & Left & Right & Left & Right & Left & Right & Left & Right \\ Reduction [dB] & 46.4 & 45.4 & 16.0 & 12.9 & 49.1 & 46.7 & 45.8 & 43.6 \\ Convergence [dB/s] & 33.5 & 33.3 & 9.9 & 7.6 & 29.5 & 28.0 & 9.0 & 8.8 \\ \hline
**Pseudo-Random** & \multicolumn{2}{c}{**FF\_FXLMS**} & \multicolumn{2}{c}{**FB\_FXLMS**} & \multicolumn{2}{c}{**FF\_FURLMS**} & \multicolumn{2}{c}{**FF\_FXNLMS**} \\ \hline Side & Left & Right & Left & Right & Left & Right & Left & Right \\ Reduction [dB] & 7.5 & 7.2 & 5.1 & 6.4 & 9.5 & 7.4 & 4.0 & 3.3 \\ Convergence [dB/s] & 2.1 & 1.6 & 2.7 & 2.9 & 2.6 & 2.4 & 0.7 & 0.4 \\ \hline
**White Noise** & \multicolumn{2}{c}{**FF\_FXLMS**} & \multicolumn{2}{c}{**FB\_FXLMS**} & \multicolumn{2}{c}{**FF\_FURLMS**} & \multicolumn{2}{c}{**FF\_FNLMS**} \\ \hline Side & Left & Right & Left & Right & Left & Right & Left & Right \\ Reduction [dB] & 2.2 & 2.0 & 1.4 & 1.9 & 2.4 & 2.1 & 1.3 & 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Attenuation and speed of convergence for a free field case for different types of noise. feed-forward systems (i.e., FF_FXLMS, FF_FXNLMS, and FF_FURLMS) is outside the cabin since it provides a larger attenuation at a higher speed of convergence. This configuration also allows for greater stability, avoiding the acoustic feedback from the control loudspeakers to the reference microphone (present when the microphone is placed inside the cabin). Using the same step size for all the systems with a fixed parameter leads to slow convergence of the feedback system FB_FXLMS, due to the need for internal modeling of the reference signal. By incrementing the step size, the feedback system represents the solution most exposed to instability. For all the pure tones and the pseudo-random noise, the FF_FURLMS algorithm shows a higher convergence speed with respect to the other algorithms, since it implements IIR filters which consider the presence of poles in the filter modeling. The performance of all the configurations worsens when the external sound source emits a random signal with no periodicity rather than sinusoidal or pseudo-random signals. In particular, the feedback system becomes ineffective because of its predictor nature which is unsuitable for this kind of noise signal. On the other hand, the feed-forward systems can reach a reduction of more than 3 dB(A) on the overall level, acting on the frequency spectrum components above 500 Hz. Finally, the comparison with the same ANC system positioned in a free field shows that the presence of the cabin limits the capability of attenuation and in some cases the speed of convergence for all the LMS-based algorithms. Methodology, F.M., A.S. and P.B. ; Investigation, F.M., A.S. and P.B. ; Writing--original draft, F.M. ; Writing--review & editing, A.S. and P.B. ; Supervision, P.F. and F.P. ; Project administration, P.F., F.P. and P.N. All authors have read and agreed to the published version of the manuscript.