Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper discusses a new framework focusing on musical acoustics simulation. Traditional numerical methods for musical acoustics simulation involve solvers for partial differential equations to model the physics of musical instruments and sound propagation. These methods are very slow. The authors present the Neuralacoustics framework which implements various components necessary to support deep learning-based approaches to musical instrument simulation. It includes various solvers for numerical models which enable dataset generation which in turn can be used to train neural networks to approximate the numerical models. The authors provide a walkthrough of their framework and how to go about training a neural operator for a specific numerical model.
strength_and_weaknesses: Strength:
- The authors identify acoustic simulation as a novel area of physics-based models where deep neural nets have not yet been widely applied. 
- They develop a complete framework to facilitate deep learning-based research by implementing existing physical models in a familiar environment (Pytorch) for deep learning researchers. This is potentially very helpful for developing a new community in the research area.

Weaknesses:
- It is not clear to me how relevant this paper is for the community at ICLR. A large portion of this paper describes the framework and how it can be used to generate data using python implementations of numerical models of instruments. However, only a small part of the paper actually talks about modeling these numerical models with deep networks which may be more relevant to the community at large. 
- Maybe this is obvious to others but I would be interested in seeing a comparison of the advantages/disadvantages of neural methods vs numerical methods for modeling the acoustics. For example, a user study comparing the outputs of two simulators, a comparison of simulation speed, etc.
clarity,_quality,_novelty_and_reproducibility: The writing is clear, but the paper reads like a software API guide for the framework at times. The content presented is novel indeed.
The authors do plan to release their framework, hence the results shown in the work should be reproducible.
summary_of_the_review: The paper describes a new framework for musical acoustics simulation which facilitates deep learning-based research. The paper goes over the various components in the framework followed by a case study of how it can be used for modeling a specific instrument type using neural operators. While I do not see any big issues in the technical details of the paper but I do find it difficult to gauge the interest in this work in the ICLR community. Hence I give the paper a rating of weak reject.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This work introduces a framework for the acoustic modeling of musical instruments. The authors present an open-source and open-access framework for the generation of numerical muscial acoustics. 
strength_and_weaknesses: I am not a person in acoustic areas. Thus, everything in this area should be new and exciting to me. However, from this description of this work, I just feel that what they are doing has been repeatedly considered by our researchers in image. Let me talk about some direct feelings about this work. 
1. I have been writing a research proposal recently. This work looks like a research proposal. It does not look like a research paper. Why do you only provide preliminary results for your research paper? Does it mean that the authors have felt that their works have some substantial drawbacks needed to be considered in the future? If this work is just a preliminary result, why do you submit your work to a research conference?
2. Actually, I cannot find more detailed explanations for the dataset in this paper. Maybe, I missed some links. I just feel that if this is an open-access framework, is it possible to provide an external link to show some results or examples?
3. Besides, the method itself for data generation is not new. I have seen many descriptions of learning mappings between function spaces. If the authors just repeat what we have done for images in acoustic signals, I do not think it can ensure an ICLR paper. Maybe, ICASSP is better. 
4. I read this paper for 1 hour. However, it looks like a technical report to me. 
clarity,_quality,_novelty_and_reproducibility: This paper provides explanations for their work. However, some important details are still missing. 
summary_of_the_review: I have shown my concerns above. This paper just reads like a technical report.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper introduces a new framework, called neuralacoustics, which uses neural networks to solve PDEs associated with the physical simulation of musical instrument sounds.
strength_and_weaknesses: Strengths:
The problem itself is interesting, and using neural nets for PDE solving is a relatively new and exciting area. I have not seen neural networks applied specifically to PDEs for musical acoustics before. It is clear that great attention is paid to implementation details and good engineering practices. Those are listed clearly, and it should facilitate easy use by the community.

Weaknesses:
This paper is hard to judge because it focuses so much on the technical details of the implementation and not on the actual novel points and results. Firstly, the authors say that "The proposed framework can be accessed here" but the footnote says "Anonymized for Submission". Many sections detail out the file structure in great depth, but I cannot even see the framework or any of the points mentioned.   In addition, it is important to share qualitative results of musical instrument sounds that were simulated. What did they sound like, what kinds of instruments were generated and so on. This kind of novel framework could be valuable to the community. However, for this publication venue, the paper should focus less on the specific implementation details, file structure, etc.
clarity,_quality,_novelty_and_reproducibility: Reproducibility is clearly the highest. This paper is designed to be an open source framework and everything is described in great detail. Clarity and quality are also fine. The application of neural net PDE solvers to music acoustics is certainly novel and interesting
summary_of_the_review: The idea is interesting and the framework could be valuable to the community. However, the paper does not actually include the framework or example results, and is focused far too much on implementation details for publication at this venue. I would suggest a more engineering focused venue, or to restructure the work to focus more on results and applicability to the community.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: Summary: The paper presents a new framework - Neuralaccoustics, which uses neural networks to solve PDEs associated with the physical simulation of musical instrument sounds. 

Strength: the topic is interesting and the proposed framework can be potentially very helpful for developing a new research community. 

Weakness: the paper presentation is more on what can be done, instead of how things can be done and there's no experimental justification on the feasibility. As pointed out by the reviewer that "the paper reads like a software API guide", "research proposal". Lacking of comparisons of advantages and disadvantages largely weakens the paper.
justification_for_why_not_higher_score: The lack of qualitative results and the less focus on methodology and too much details on implementation make this paper not a good fit for this venue.
Decision: Reject