Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: In this paper, the authors propose a diffusion-based system that can directly generate binaural signals. Based on analysis of the way sound propagates and humans perceive spatial impressions of sound, the authors designed a two-step system. The proposed system is evaluated by both objective measures and subjective evaluations, both of which shows that the proposed system is trained successfully. 
strengths_and_weaknesses: Strength: The proposed system is designed based on a nice intuition. It’s novel, and effective. The design and result of the experiment are solid. The writing is good, too.

Weakness: Directly synthesizing the binaural signals is not practical as of now given its high computation complexity. (But I do not think this is not a critical weakness)

questions: L162-163: Just curious, Why? White noise has equal energies all over the frequency. 
Is it bc the test signals have, due to our auditory perception, more energy on
the low-frequency region? It could be useful to discuss this

In general: There are many symbols (x, y, with bars and hats etc). I found it quite confusing at the beginning and once misunderstood something was wrong. 

limitations: (Mentioned in the Weakness)
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper studies the problem of binaural audio synthesis, and proposes to decompose the binaural audio into a common part that shared by the left and right channels as well as a specific part that differs in each channel. A two-stage framework based on diffusion models is proposed to synthesize the two channels, respectively. Experiments on a benchmark dataset demonstrate the effectiveness of the proposed framework.
strengths_and_weaknesses: Strengths: The idea to use diffusion models for this task is interesting, and the proposed two-stage framework is well-motivated. Generally, the paper is clearly written and easy to understand. Table 1 clearly shows that the proposed method based on diffusion models compares favorably to prior methods including the latest neural method WarpNet [29]. The main contribution/strength of this paper is introducing a technique---diffusion model, which is demonstrated to be powerful in many other areas, to this existing task of binaural sound synthesis, and achieves some gains.

Weakness: The motivation for dividing the binaural synthesis problem into two stages can be better discussed. For example, in Figure 1, it would be useful to explicitly illustrate what information is used in the first stage and what information is used in the second stage, instead of just showing the waveforms. Moreover, diffusion models are powerful and lead to good gains in terms of binaural sound synthesis results, it would be important to discuss why it is helpful and suitable for this specific task, compared to prior methods as well as its limitations. No limitations or failure cases are currently discussed across the paper and only one sentence discusses potential societal impact.
questions: Diffusion models are powerful and lead to good gains in terms of binaural sound synthesis results, it would be important to discuss why it is helpful and suitable for this specific task, compared to prior methods as well as its limitations. 
limitations: No limitations are discussed in the paper. There is only one sentence that discusses the societal impact: The negative impact of our method might be the abuse of binaural speech synthesis, which is very vague and unclear.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This is a paper that applies the diffusion model to a binaural audio generation task. The authors use a two-stage method to accomplish their task and use a modified wavenet-like structure as their model architecture. After thorough and detailed experiments, the authors achieve the best results among the listed models, which nicely support their conclusions. The contributions of the paper include: 
- A new two-stage framework is used to generate high-quality binaural audio, with the assistance of a diffusion model.
- The experiment results show that the proposed method achieves excellent performance on this task.



strengths_and_weaknesses: Strengths:
- This paper is easy to follow. The authors give a detailed background of the task, a clear definition of the problem, and a comprehensive explanation of the proposed method.
- The experiments are adequate. The authors have verified the feasibility of their method through comprehensive experiments and subjective and objective evaluations, which support their conclusions very well.


Weaknesses:
- The values of the standard deviations for all results in Table 3 seem to be a bit too large, which indicates that there may be a large disagreement among raters about the experimental results. Such results may be unconvincing.

Besides that, there are no significant weaknesses in this paper, but there are some issues that are difficult to explain and could be potential weaknesses, which will be mentioned in the next section.


questions: - The authors mention that they use warped binaural audio as conditional input, but never explain how this audio is warped. We would like to know if these audios are warped using traditional DSP methods, or other additional methods? Does the use of such conditional inputs lead to some kind of information leakage, and have the authors performed ablation experiments on this?
- The authors use the pattern of frequency destroying they found in the forward process of the diffusion model to explain why they use a two-stage generation method. However, in Table 2, the frequency-related evaluation metric MRSTFT of the proposed method is not as good as that of the single-stage model. Can the authors explain this phenomenon?
- Have the authors compared the generation speed of single-stage and two-stage models and is there a significant difference between the two?



limitations: The authors make a good statement about the limitations of their work.
review_title: null

Meta-review: metareview: The use of diffusion models for binaural audio synthesis is interesting and the two-stage design is novel and well motivated. The authors also addressed the reviewers concerns.
confidence: Certain
Decision: Accept