Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The paper studies the effect of controlling the musical attributes of the music generated by the FIGARO and which music features contribute more to the music quality when controlled like instruments, chords , mean pitch etc
strengths_and_weaknesses: Strengths 
1. The research questions are clear and consistent with the experimental design and findings 
2. The methodology and the controlled musical attributes are easy to describe and reproduce (did not see the code)
3. I like how figure 1 is explained clearly and is intuitive for a person who is not familiar to the music technical terms 

Weaknesses
1. The correlations were not easily interpretable, would like to see some qualitative examples or descriptions of what they actually mean 

suggested_changes: 1. Examples of the descriptions used for control would be helpful for the reader to visualize the task 
2. It will be helpful to introduce the relationship between the musical attributes and the music generated to better understand the correlations mentioned in the paper
3. I suggest the authors had some background literature on controlled music generation to better position the study in the field
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper attempts to control the musical properties of music generated by the FIGARO model, while evaluating the success of the control and the overall musical quality.
strengths_and_weaknesses: **Strengths**
- The idea of this paper employing musical properties to control music generation is interesting
- In the experiment, the description of the data processing is sufficient

**Weaknesses**
- Authors should explain how this algorithm differs from conditional generative models
- In the method, the author should introduce how the music attributes are embedded in the FIGARO model.
- In the experimental part, the author should introduce the detailed relationship between various indicators and music properties.

suggested_changes: 1) Authors should explain how this algorithm differs from conditional generative models
2) In the method, the author should introduce how the music attributes are embedded in the FIGARO model.
3) In the experimental part, the author should introduce the detailed relationship between various indicators and music properties.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper studies the relationship between individual musical attributes and the quality of FIGARO generated music. The results show that many musical attributes are correlated, and FIGARO maintains a consistent music quality when the individual attribute is controlled.
strengths_and_weaknesses: ## Strengths and weaknesses

#### Clarity

1. Overall the paper is clear and easy to read.
2. How are the attributes represented in the experiment? For example, is the attribute "Chords" a number or a vector?

#### Correctness

1. The claims and conclusions are justified by the findings.

#### Reproducibility

1. The paper includes findings from an empirical experiment, but the code and data are not open-sourced.

#### Follows basic requirements

1. This paper follows the basic formatting requirements.
suggested_changes: ## Suggested changes

1. What does "controlling the generated music" mean in Section 2 first paragraph?
2. It would be great if the paper could explain the columns of Table 1 in the main text.
review_title: null

Decision: Invite to present