Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This paper presents a deep model for learning to predict the sound that would be heard at a given target position, assuming that a reference position and the sound heard at the reference position are given. 

The authors presents a MLP for learning forward RIR and inverse RIR, where the reference sound is first projected to a
virtual implicit sound source field with inverse-RIR, and then the virtual implicit sound source field is re-projected to the target sound with forward-RIR. 

The authors also used three datasets to evaluate the performance of the proposed methods and benchmark them with several baseline methods, in terms of MSE, and TF-MSE. 
strength_and_weaknesses: Strength of the paper: 
This paper is overall well presented. The proposed idea looks ambitious but interesting. 
The results in terms of MSE or TF-MSE look impressive. The authors also produced several datasets which they plan to release publicly and will be very useful for the research community.

Weakness of the paper:
The authors consider the virtual microphone positions by simply multiplying a gain factor. The potential issues with this method include (1) this may not be a good way for dealing with early reflections and late reverberations; (2) how to choose these gain parameters in a practical scenario. In practice, the sound reflections from room surfaces can be from multiple directions. How can the gain parameters approximate these reflections or various time delays? Is there a principled way of choosing the gain parameters and are the two parameters a reasonable choice?
The authors showed the MSE and TF-MSE results, which can somewhat show how good is the performance of the compared methods, but may not be fully reflecting the truth. In the RIR shown in the paper, the early reflections seem to follow the direct sound immediately. I am expecting some time delays between the direct sound and early reflections, and also the early reflections are often more sparse than the late reflections. This does not seem to be case for the simulated RIR or the real room RIR. Not sure why the authors did not plot the ground truth RIR in the test case (considering the fact that they do have the ground truth RIRs that were used for computing the MSE). Showing both the estimated RIR and the ground truth can provide useful information on how good is the RIR learned by the network.     
clarity,_quality,_novelty_and_reproducibility: The paper is generally clear although some details are still missing, for example, the choice of the gain for virtual microphone, and its potential limitations, and how they are chosen in a practical scenario. 

Novelty is very good as learning to predict sound in a new position given the reference sound and its position is a new idea. Using robot to collect the sound recordings in this setting seems to be feasible. 

The authors plan to release their datasets. It would be good for reproducibility if they could release all the codes and models trained.  
summary_of_the_review: Overall, this is a good paper with interesting ideas and some preliminary results. However, the paper also has several aspects for improvements which would lead the paper from good to excellent such as the validation of assumptions made. The visual results of RIR doe not seem to be promising although the MSE results look good.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: In any given acoustic enclosure or a room, the sound produced at a certain location undergoes multiple reflections and travels a certain distance before arriving at a receiver kept elsewhere in the room. Because of this, the sound recived at a distant receiver is not the same as the sound at the source. One of the major problems in room acoustics is estimating the sound at a receiver at a known location given the original sound and the location of the sound sources under a specified room geometry. Typical signal processing methods employ ray tracing or wave tracing methods to account for reflections and under the linear time-invariant (LTI) model of a room, the impulse response between a source and a receiver with known locations (RIR) is estimated. The signal at the reciver would then be a simple convolution of the impulse response with the original source of sound. 

Recently, several authors have proposed using deep learning for estimating the RIR. The main drawback of such approaches is the need for a large labelled training set which maps source and receiver location to a specific RIR and this network would be trained specifically for a given enclosure. Different enclosures would need different networks. 

In this work the authors propose to estimate the RIR between any two random locations based on sampling the room with up to 500 locations using a robot which would move to each location and record the audio along with receiver location. The authors propose to use this data to compute RIR between two receiver locations, instead of computing RIRs between a source and receiver using an MLP and a curious set of design choices. The paper is not very well written and needs effort to understand.  
strength_and_weaknesses: Strengths:
1. Interesting idea to estimate Receiver to receiver Impulse response. 

Weakness:
1. The overall design simply does not make sense. Irrespective of the reverberation properties of the enclosure the authors propose to estimate RIRs of length 257 samples corresponding to 16 kHz sampling rate. This amounts to an RIR of duration less then 20ms. Typical residential rooms would have reverberation time (RT60) - measured as the time taken for sound to reduce to 60 db from its original value after the sound source has stopped. Thus longer RIRs are needed to even characterize such room attributes. The choice of using the same MLP to predict RIR of 2 other virtual distances which twice and thrice farther apart is also not well justified and is presented with a hand wavy argument - simulating ray tracing method (ISM). It is not at all clear how the authors are calling whatever the network is estimating as RIR as there is no evaluation comparing this to actual RIR. 
2. Essentially this approach is mapping a location in space to an RIR using an MLP. This is an multi-valued function and as such admits multiple RIRs for the same location based on the source location which is ignored in this work. 
3. It is assumed that the robot would move to each location and capture the exact same audio each time. It is assumed that is easy for the robot to record its location. This obviously means the source of sound is being precisely controlled to start and stop and required times to make sure the robot records when it is stationary. This problem setup does not make any practical sense. It is hard to imagine a case where  it is hard to also jot down the source location. With a known source location the estimation would be so much well defined. 
4. I also believe with the limited sound source chosen for evaluation (7 clips in simulation and probably only 2 in real data) the network is simply memorizing and generalization is not well studied at all. From the appendix it is clear that the training and test locations, although different are roughly in the same neighborhood. 
5. The overall presentation is rather confusing with terms like "inverse-RIR" and "Forward RIR" and it feels like these terms are set arbitrarily and is rather confusing. 

clarity,_quality,_novelty_and_reproducibility: I am not sure how reproducible this work is. In my opinion the problem formulation and network design and evaluation are flawed.
summary_of_the_review: Overall, Because of the Weakness pointed out in the above section I believe the design and problem formulation are flawed and the presentation is unnecessarily complicated. The evaluation is also not satisfactory.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The authors propose a supervised learning approach to learning the room impulse response function, specifically by leveraging multi-layer perceptron (MLP). Evaluation on a range of datasets shows that the proposed approach outperforms several recent approaches.
strength_and_weaknesses: Strengths
1. Evaluation across a variety of datasets.
2. Proposed method significantly outperforms comparative baselines.
3. Promised release of the referenced datasets.


Growth Opportunities
1. I don't understand this statement. "We also assume all receivers record the same sound content, so the sound at any target position can be inferred by the sound from any reference position." Isn't the sound received a function of the location and the propagation characteristics of the room?
2. The motivation of using specifically 2 virtual receiver positions in 3.2 is unclear.
3.  The drawbacks of classical RIR estimators is misleading as the proposed approach required significant number of labeled pairs for training, a key limitation.
4. No justification is provided for the design of the specific MLP in Section 3.2 is provided. It would be also helpful to understand how sensitive the solution is to the architecture.
5. Results of figure 5.a is confusing. If the proposed approach is truly independent of the number of receiver positions, it would be worth establishing how low the number could be. 
6. Additional testing on other room sizes and configurations would be helpful, especially with respect to different absorbent/reflection surfaces.
clarity,_quality,_novelty_and_reproducibility: 1.The authors propose to release the datasets to the public, helping reproducibility. 

2. Organization/formatting could be better. Long-distance cross-references also affect clarity.

3. Some of the results (e.g., Table 7) are presented without an adequate analysis.

summary_of_the_review: The paper proposes a novel approach of learning room impulse response by leveraging a mobile robot to collect additional samples of the same source(s). Writing and clarity could be strengthened as well as addressing some of the aspects highlighted above to make it a strong paper
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The author proposes a receiver-to-receiver sound neural room acoustics rendering field, which is different with previous works focusing on source-to-receiver RIR prediction. To be specific, the author decomposes the receiver-to-receiver RIR prediction into two parts: inverse-RIR and forward RIR, which is a very interesting design.
strength_and_weaknesses: Strength:
1. The paper solves the RIR prediction in a novel view: modeling the sound field with some positions with acoustic information, which is somewhat a interpolation method. Receiver-to-receiver sound field modeling requires no knowledge about the source and can support more complex scene.
2. The author decomposes the receiver-to-receiver RIR prediction into two parts: inverse-RIR and forward RIR, which is a very interesting design and has physical intuition.
3. The experiments are convincing and adequate, consisting of several datasets with different source number. The ablation verifies the effectiveness of each proposed technique.

Weakness:
1. Since the impulse response is the response of an impulse, it is a time-delay function (i.e., the impulse response assumes the impulse is emitted at the time 0, and the receiver gets the signal at positive (delay) timestamp). When we predict the waveform for a new position p_new, we convolve the waveform of a known position p_known with two IRs (inverse-RIR and forward RIR). In this case, the waveform will be further delayed, which means the method cannot handle the case that p_new is closer to the source than p_known. Maybe there are some tricks on applying the convolution?
2. In the section 3.4 (second paragraph), it seems that some subscripts are wrong (e.g., t^inv_d < t^inv_d < t^inv_l), which makes the physical constraint hard to understand. Can you state it more clearly?
3. Although the space limitation can be a reason, I think it is necessary for a paper to have a conclusion.
4. Just a question, will the dataset used in the paper be released to make the future work on this direction easier?

clarity,_quality,_novelty_and_reproducibility: The method is clear and novel. The paper seems to be easy to follow if the dataset is released.
summary_of_the_review: The paper is novel and solid. However, there are still some missed details that can be further clarified.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper presents a deep model that learns a continuous receiver-to-receiver acoustic neural field for predicting the sound to be heard at novel positions. The prediction is made conditioned on that a reference position and the sound heard at the reference position are given. The ideal is interesting, experimental results look good and the authors plan to release the data sets. The reviewers also see that more extensive experimental evaluation would be needed to validate the method and thus strengthen the work. Other comments are related to the constraint that different enclosures would need different networks, time-delay considerations and problem formulation. 
justification_for_why_not_higher_score: The idea is of interest. But the evaluations are insufficient, and some assumptions will prevent it from real applications.
Decision: Reject