# Musical Mix Clarity Prediction Using Decomposition and Perceptual Masking Thresholds
## 4 Model Evaluation
Therefore, short analysis frames were not used in any of the masking calculations and thus could not be responsible for the difference in performance between the L2 PM and L3 PM clarity model variants. To confirm that the difference in scale-factor bands was largely responsible for the weaker correlation of the L3 PM clarity model, a modified version of the L3 PM was devised. This calculated the energy and masking thresholds following the L3 PM [23,24], however, rather than calculating the SMR directly in scale-factor bands which approximate critical bandwidth, the masking thresholds were spread back across the spectral bins of the Fourier transform and the SMR was calculated for the linearly distributed bands specified in the  
Figure 4: Spearman rank correlation between overall masking score ranks calculated using the L3 PM clarity model and median clarity scores. L2 PM [23]. This saw an increase in performance to a level similar to, but lower than the L2 PM clarity model, achieving _rho_ = \(-0.8088\), \(p<0.01\) and is shown in Figure 5.  
The modified L3 PM clarity model improves the position of the L3 PM clarity model's most severe outlier, '94414', though a number of outliers remain. The remaining differences between the L2 PM and modified L3 PM clarity models were due to the difference in how the models calculate the masking threshold. Whilst this implementation of the clarity model had a slightly weaker Spearman and Pearson correlation (\(r=-0.7868\), \(p<0.01\)) than the L2 PM clarity model, the Spearman and Pearson correlation coefficient values are similar, suggesting a more linear relationship between the modified L3 PM clarity model and the subjective scores. Further to testing the clarity model variations' correlations to the subjective data, they were also evaluated using the independent dataset (Section 3.4). Figures 6-8 show the scores calculated for audio examples at the various levels of degradation for each of the three degradation methods. Box plots show median and interquartile range as well as outliers at each degradation level. Figure 6 shows clarity scores calculated by the L2 PM clarity model for the independent dataset. The addition of pink noise sees the addition of broadband residual energy resulting in the masking of transient and steady-state components of the stimuli. As such, the masking scores progressively rise as more noise is added, until they reach a ceiling level where the TSS component is maximally masked. This ceiling causes a convergence of the clarity scores for the tested stimuli, with the tracks scoring a lower MCS in the subjective test tending to see less change when degraded by pink noise. This suggests that how noise-like the stimuli were may have had an influence on the MCSs they received from listeners. Figure 5: Spearman rank correlation between overall masking score ranks calculated using the modified L3 PM clarity model and median clarity scores. The addition of reverberation had a similar though less extreme effect as adding pink noise. Unlike pink noise, the reverberated signal is still derived from and therefore correlated to the original signal. If viewed using a spectrogram, reverberation is somewhat akin to blurring an image where the energy is smeared over time, and to a lesser extent frequency. This smeared energy is largely characterised by the separation system as residual energy, increasing the level to which the TSS component is masked by the R component, and decreasing the level to which the R component is masked by the TSS component. The addition of reverberation tended to increase the masking score of the stimuli. However, a greater effect is seen for stimuli which received a higher MCS similarly to the addition of pink noise. Moreover, masking scores for stimuli which did not contain strong transient onsets from things such as drum hits, such as '137167','15541', and '94414' also showed less difference than those which did.