Figure 6.

Results of hyperparameter tuning obtained with SMNN 5. (a) Batch size $N_{\mathit{batch}}$, (b) number of frames per input vector $N_{\mathit{stack}}$, and (c) offset between two consecutive input vectors $N_{\mathit{slide}}$. Left: TPR vs. FPR scores for hyperparameter tuning (the value closest to the TPR = 1 and FPR = 0 corresponds to the best perfromance). Middle: distance $d=\sqrt{{\left(1,-,\mathrm{TPR}\right)}^2+{\left(\mathrm{FPR}\right)}^2}$ with respect to TPR = 1 and FPR = 0 (the lowest value corresponds to the best performance). Right: classification accuracy (the highest value corresponds to the best performance). Red dot denotes the parameter with the best performance.