# Algorithmic Music for Therapy: Effectiveness and Perspectives
## 3 Results
### Phase 2
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**All Subjects**} \\ \hline  & **HH** & **HC** & **CH** & **CC** \\ HH & 1 & 0.28313 & 0.001 & 0.0003 \\ HC & 0.28313 & 1 & 0.05217 & 0.02241 \\ CH & 0.001 & 0.05217 & 1 & 0.79334 \\ CC & 0.0003 & 0.02241 & 0.79334 & 1 \\  & \multicolumn{4}{c}{With musical training} \\  & **HH** & **HC** & **CH** & **CC** \\ HH & 1 & 0.78885 & 0.00284 & 0.00909 \\ HC & 0.78885 & 1 & 0.03044 & 0.07029 \\ CH & 0.00284 & 0.03044 & 1 & 0.95809 \\ CC & 0.00909 & 0.07029 & 0.95809 & 1 \\  & \multicolumn{4}{c}{Without musical training} \\  & **HH** & **HC** & **CH** & **CC** \\ HH & 1 & 0.48585 & 0.1659 & 0.02745 \\ HC & 0.48585 & 1 & 0.68641 & 0.21269 \\ CH & 0.1659 & 0.68641 & 1 & 0.53559 \\ CC & 0.02745 & 0.21269 & 0.53559 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Chi-squared test (with \(\alpha=0.05\)) assessing whether the perception of the music (music or non-music) was different for all the possible combinations of human/computer performer/composer. HH = music composed and performed by a human; HC = music composed by a human and performed by a machine; CH = music composed by a machine and performed by a human; CC = music composed and performed by a machine. that the composer can be identified when the performer is also a computer. On the other hand, it is difficult for the participants to identify the composer when a human performs music composed by a computer. The second row of plots performs a similar analysis but focuses only on the participants with musical training. Focusing on plots (e)-(h), it is possible to strengthen the previous findings. Finally, the fourth plot (h) makes it clear that the participants can identify the computer as the composer when the music is performed by the computer itself. This analysis seems to suggest that the performer is the key element. This is quite evident when focusing on the pairs of plots (e)/(f) and (g)/(h), where a different performer makes it simpler for the participants to identify the composer. While usually the performer adds a layer of expressiveness on top of the composed music, in order to reduce potential differences in  
Figure 4: Number of participants in the considered groups (whole group, with musical training, and without musical training) who identified the composer of the music they listened to as Human or Computer. HH = music composed and performed by a human; HC = music composed by a human and performed by a machine; CH = music composed by a machine and performed by a human; CC = music composed and performed by a machine; w/ = with; w/o = without. execution by the human with respect to the machine (expressiveness, dynamics, tempo, etc. ), the human performer was asked to perform the musical pieces avoiding variations and seeking both regularity and uniformity in the execution. Still, for the listener, the performer has a relevant effect in the identification of the composer of the music. The last row of plots in Figure 4 considers the participants without musical training. For this subset of participants, the four plots suggest that it is generally difficult for them to recognize a composer of music, except for music composed by a human and performed by a computer (j). Additionally, it is interesting to compare the plots (e) and (h) reported in the second row (participants with musical training) with those in the third row (i) and (l) (participants without musical training). From this comparison, it seems that when the performer and the composer are the same entity (human or computer),it is simple for the participants with musical training to correctly identify the composer. On the other hand, participants without musical training still have some difficulties in identifying the composer. To validate the aforementioned qualitative findings, we performed a statistical analysis.