# Generating Music with Data: Application of Deep Learning Models for Symbolic Music Composition
## 2 Materials and Methods
### Blind Test  
The blind test was developed in three languages (Portuguese, English, and French) and was distributed through the SurveyMonkey online platform through three web forms (one for each language). For the blind test, three types of populations were initially selected:  
* People who listen to classical music often.
* Classically trained musicians.  
The criterion adopted for choosing the aforementioned population was based on the hypothesis that musicians with a classical background and people who listen to classical music frequently have a greater ability to distinguish the compositional origin of a piece of music. The purpose of this criterion was to add details for the final analysis of the research.  
All subjects had a total of five (number of generative models +1) musical excerpts to be heard, where one of them (+1) derived from a composition written by a human, randomly selected within the data used for training. A musical excerpt was collected from each model  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Learning rate & 0.0005 \\ Batch size & 64 \\ Learning rate scheduler & Cosin \\ Weight decay & 0.1 \\ Gradient accumulation steps & 8 \\ Number of attention heads for each attention layer in the transformer encoder & 8 \\ Number of hidden layers in the transformer encoder & 6 \\ Embedding and hidden states dimensionality & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of hyperparameters for training the Custom GPT2 model.  
without any preconditioning. The musical excerpts were the same for the entire tested population, though they were initially ordered arbitrarily.  
The following five questions were proposed for each musical excerpt:  
* On a scale of 0 to 10, how musical do you think the piece of music heard is? \(\circ\) Options from 0 to 10.
* Do you believe this song was made by a human or a computer? \(\circ\) Binary Response (Human or Computer).
* Would you say that the musical excerpt heard resembles classical music? \(\circ\) Binary Response (Yes or No).
* (Optional) Add any thoughts you might have about the excerpt heard. \(\circ\) Free text. It's worth highlighting that some interesting answers of the optional free text question are listed on Appendix A.  
After the end of the evaluations of the musical excerpts, the participants were directed to the following set of questions related to their musical experiences:  
* How would you rate your musical sensitivity? \(\circ\) Options from 0 to 10.
* How often do you listen to classical music? \(\circ\) I don't listen to classical music. \(\circ\) Sometimes. \(\circ\) Often.
* Do you have any practical experience with music? \(\circ\) No. \(\circ\) Yes, I can make music with my voice or a musical instrument. \(\circ\) Yes, I study/I studied music theory (conservatory, music school, self-taught). \(\circ\) Yes, I study/studied at a music university  
The test consisted of a total of 23 questions, 20 (5 musical excerpts \(\times\) 4) questions related to the musical excerpts heard and 3 questions related to musical experience.