Coupled Recurrent Models for Polyphonic Music Composition
This work describes a novel recurrent model for music composition, which accounts for the rich statistical structure of polyphonic music. There are many ways to factor the probability distribution over musical scores; we consider the merits of various approaches and propose a new factorization that decomposes a score into a collection of concurrent, coupled time series: "parts." The model we propose borrows ideas from both convolutional neural models and recurrent neural models; we argue that these ideas are natural for capturing music*s pitch invariances, temporal structure, and polyphony. We train generative models for homophonic and polyphonic composition on the KernScores dataset (Sapp, 2005), a collection of 2,300 musical scores comprised of around 2.8 million notes spanning time from the Renaissance to the early 20th century. While evaluation of generative models is know to be hard (Theis et al., 2016), we present careful quantitative results using a unit-adjusted cross entropy metric that is independent of how we factor the distribution over scores. We also present qualitative results using a blind discrimination test.
 The paper is well written and presented, giving a good literature review and clearly explaining the design decisions and trade-offs. The paper proposes a novel factorisation approach and uses recurrent networks. The evaluation is both quantitative and qualitative. The qualitative experiment is interesting, but there is no information given about the level of musical training the participants had. You would expect very different results from music students compared to the general public. How did you control for musical ability/ understanding? The paper has a refreshing honesty in its critical evaluation of the results, highlighting fundamental problems in this field. Overall, while I am not an expert in musical composition and machine learning, the paper is clear, and appears to be advancing the art in a reliable fashion.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Coupled Recurrent Models for Polyphonic Music Composition
This work describes a novel recurrent model for music composition, which accounts for the rich statistical structure of polyphonic music. There are many ways to factor the probability distribution over musical scores; we consider the merits of various approaches and propose a new factorization that decomposes a score into a collection of concurrent, coupled time series: "parts." The model we propose borrows ideas from both convolutional neural models and recurrent neural models; we argue that these ideas are natural for capturing music*s pitch invariances, temporal structure, and polyphony. We train generative models for homophonic and polyphonic composition on the KernScores dataset (Sapp, 2005), a collection of 2,300 musical scores comprised of around 2.8 million notes spanning time from the Renaissance to the early 20th century. While evaluation of generative models is know to be hard (Theis et al., 2016), we present careful quantitative results using a unit-adjusted cross entropy metric that is independent of how we factor the distribution over scores. We also present qualitative results using a blind discrimination test.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper is well written and presented, giving a good literature review and clearly explaining the design decisions and trade-offs. The paper proposes a novel factorisation approach and uses recurrent networks. The evaluation is both quantitative and qualitative. The qualitative experiment is interesting, but there is no information given about the level of musical training the participants had. You would expect very different results from music students compared to the general public. How did you control for musical ability/ understanding? The paper has a refreshing honesty in its critical evaluation of the results, highlighting fundamental problems in this field. Overall, while I am not an expert in musical composition and machine learning, the paper is clear, and appears to be advancing the art in a reliable fashion.
assistant
 The paper is well written and presented, giving a good literature review and clearly explaining the design decisions and trade-offs. The paper proposes a novel factorisation approach and uses recurrent networks. The evaluation is both quantitative and qualitative. The qualitative experiment is interesting, but there is no information given about the level of musical training the participants had. You would expect very different results from music students compared to the general public. How did you control for musical ability/ understanding? The paper has a refreshing honesty in its critical evaluation of the results, highlighting fundamental problems in this field. Overall, while I am not an expert in musical composition and machine learning, the paper is clear, and appears to be advancing the art in a reliable fashion.