# DExter: Learning and Controlling Performance Expression with Diffusion Models
## 5 Evaluation
### Quantitative Evaluation
#### 5.1.3 Results  
In Table 2, we compare our model with the samples from two existing performance-rendering systems: BasisMixer [11] (BM) (we applied the Basis Mixer model with LSTM architecture trained on the ASAP corpus) and VirtuosoNet [12] (VN) (the applied model is the sign with a default tempo and composer settings agreed on by the author). The results were rendered using the same testing set as ours and shown as means and standard deviations. \begin{table}
\begin{tabular}{l c c c} \hline  & **Basis Mixer [11]** & **VirtuosoNet [12]** & **DExter (Ours)** \\ \hline _Deviation Multiple_ (\(\rightarrow\)0) & & & \\ \hline articulation.key\_overlap\_ratio & \(\mathbf{0.62\pm 3.15}\) & \(1.92\pm 2.72\) & \(2.24\pm 2.76\) \\ asynchrony.pitch\_correlation & \(-1.19\pm 1.41\) & \(-1.67\pm 1.25\) & \(\mathbf{-1.17\pm 1.66}\) \\ asynchrony.delta & \(\mathbf{4.10\pm 1.78}\) & \(4.38\pm 1.63\) & \(4.43\pm 2.21\) \\ dynamics.agreement & \(-0.07\pm 1.33\) & \(-0.40\pm 1.30\) & \(\mathbf{-0.002\pm 1.05}\) \\ dynamics.consistency & \(-\mathbf{0.67\pm 1.64}\) & \(-1.07\pm 1.56\) & \(0.73\pm 2.21\) \\ dynamics.ramp\_correlation & \(-0.36\pm 2.54\) & \(0.65\pm 1.96\) & \(\mathbf{-0.28\pm 2.44}\) \\ pedal.onset\_value & - & \(-1.16\pm 1.68\) & \(-1.39\pm 2.13\) \\
**tempo\_curve** & \(-\mathbf{0.10\pm 2.44}\) & \(0.52\pm 2.48\) & \(0.72\pm 2.65\) \\
**velocity\_curve** & \(-0.67\pm 1.48\) & \(\mathbf{0.15\pm 1.02}\) & \(1.48\pm 2.12\) \\ \hline _KL Divergence_ (\(\downarrow\)) & & & \\ \hline articulation.key\_overlap\_ratio & \(\mathbf{0.92\pm 2.15}\) & \(1.66\pm 6.89\) & \(1.64\pm 3.63\) \\ asynchrony.pitch\_correlation & \(0.14\pm 0.278\) & \(\mathbf{0.13\pm 1.25}\) & \(0.20\pm 0.33\) \\ asynchrony.delta & \(4.04\pm 5.15\) & \(4.83\pm 9.58\) & \(\mathbf{1.29\pm 3.16}\) \\ dynamics.agreement & \(0.10\pm 0.04\) & \(0.09\pm 0.04\) & \(\mathbf{0.06\pm 0.04}\) \\ dynamics.consistency & \(0.12\pm 0.23\) & \(\mathbf{0.06\pm 0.07}\) & \(0.28\pm 0.49\) \\ dynamics.ramp\_correlation & \(1.54\pm 5.43\) & \(\mathbf{0.35\pm 1.01}\) & \(0.42\pm 1.13\) \\ pedal.onset\_value & - & \(0.34\pm 1.45\) & \(0.33\pm 0.36\) \\
**tempo\_curve** & \(0.98\pm 2.55\) & \(\mathbf{0.65\pm 1.86}\) & \(1.26\pm 5.66\) \\
**velocity\_curve** & \(0.16\pm 0.21\) & \(\mathbf{0.10\pm 0.06}\) & \(0.71\pm 1.37\) \\ \hline _Pearson’s Correlation_ (\(\uparrow\)) & & & \\ \hline articulation.key\_overlap\_ratio & \(-0.01\pm 0.13\) & \(0.05\pm 0.16\) & \(\mathbf{0.11\pm 0.17}\) \\ asynchrony.pitch\_correlation & \(0.33\pm 0.25\) & \(0.55\pm 0.17\) & \(\mathbf{0.57\pm 0.25}\) \\ asynchrony.delta & \(0.17\pm 0.22\) & \(\mathbf{0.29\pm 0.19}\) & \(0.28\pm 0.21\) \\ dynamics.agreement & \(0.02\pm 0.87\) & \(0.04\pm 0.84\) & \(\mathbf{0.11\pm 0.79}\) \\ dynamics.consistency & \(0.92\pm 0.17\) & \(0.91\pm 0.13\) & \(\mathbf{0.92\pm 0.15}\) \\ dynamics.ramp\_correlation & \(0.04\pm 0.76\) & \(0.12\pm 0.80\) & \(\mathbf{0.14\pm 0.73}\) \\ pedal.onset\_value & - & \(0.01\pm 0.13\) & \(0.02\pm 0.14\) \\
**tempo\_curve** & \(0.02\pm 0.13\) & \(0.09\pm 0.19\) & \(\mathbf{0.19\pm 0.17}\) \\
**velocity\_curve** & \(0.08\pm 0.23\) & \(0.21\pm 0.27\) & \(\mathbf{0.27\pm 0.23}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative expression metrics in the categories of articulation, asynchrony, dynamics, pedaling, plus global tempo and velocity curves. The columns represent different models, and the rows are divided into blocks according to the three different evaluation metrics, with each block detailing the outcomes for all features. Note that each generated performance was compared with multiple human ground-truth interpretations. \(\rightarrow\)0 means a value close to 0 is better. \(\downarrow\) indicates a lower value is preferred while \(\uparrow\) is otherwise. The numbers in bold are the best performance on the metric. Overall, DExter showed commendable results, particularly in correlation across almost all performance dimensions, especially in capturing the global curve of tempo and velocity.