From: NIPS.cc/2018/Workshop/IRASL
title: Acceptance Decision
decision: Reject

From: NIPS.cc/2018/Workshop/IRASL/Paper42/Authors
title: Authors' Response
comment: Thank you for your feedback on our submission.

We were not aware of the DCUnet, as described in https://openreview.net/forum?id=SkeRTsAcYm. We will review it and make mention of it as an unpublished ICLR submission for the camera-ready version (should it be accepted).

We will also make sure to build upon the results already obtained, as mentioned in our previous responses, in order to facilitate a more rigorous analysis.

From: NIPS.cc/2018/Workshop/IRASL/Paper42/AnonReviewer2
title: The method shows promising results even though it lacks novelty
review: Summary:

The authors propose to apply the Wave-U-Net architecture proposed by Stoller et al for the task of speech enhancement. Stoller et al proposed the Wave-U-Net architecture for music vocals and accompaniment separation. They obtain state-of-the-art on the Voice Bank corpus (VCTK) dataset.

The authors should pay more attention to the fact that very similar architecture has been already used for speech enhancement and yields state-of-the-art on the DEMAND dataset https://openreview.net/forum?id=SkeRTsAcYm . This is achieved by using a Deep Complex UNet along with a phase aware loss function and masking operation which activates the modulus of the masks using tanh. It is very important to mention that the latter is not yet published and is an ICLR submission.

as mentioned by Paper42 AnonReviewer1, it is true that no newer insights have been provided in the paper. The authors should build upon the obtained results and try to provide a good and rigorous analysis, which might become their main contribution, if they want to extend their submission to a conference paper.
rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper42/Authors
title: Authors' Response
comment: Thanks for your review of our submission.

In response to the Cons:

> We agree about the expectation, but providing experimental evidence is still a contribution. Furthermore, the Wave-U-Net was not a superior model for voice/music separation, but it is for speech denoising, according to our experiments. We would also argue that this contributes to the recent trend of time-domain audio processing, and that this experiment contributes to the workshop topic of robust speech processing through effective denoising.

> The main insight of this paper is that the Wave-U-Net provides a new state-of-the-art in speech denoising. We are in the process of extending the ablation study, which should hopefully extend the insight form that perspective.


In response to the Minor Comments:

- The line 15 typing error has been corrected for the camera-ready version (should the paper be accepted).

- The inclusion of a figure 1 reference will be corrected, for the camera-ready version, in the more comprehensive description of the Wave-U-Net architecture discussed in our reply to the previous review.

- Put better, line 57 means that the convolutional process, which includes zero-padding, is followed by a LeakyReLU activation, with the exception of the final one, which uses tanh.
More specifically, for K sources to be estimated, a 1D convolution of filter size 1 with K · C filters is applied to convert the stack of features at each audio sample into a source prediction for each sample, followed by a tanh nonlinearity to obtain a source signal estimate with values in the interval (−1, 1). All convolutions except the final ones are followed by LeakyReLU non-linearities.
This will be included in the camera-ready version as part of the effort to provide a more comprehensive description of the Wave-U-Net architecture.

From: NIPS.cc/2018/Workshop/IRASL/Paper42/Authors
title: Authors' Response
comment: Thank you for reviewing our submission.

In response to each of your 6 suggestions for improvement, please see the below:

1. We will endeavour to provide a more comprehensive description of the Wave-U-Net architecture for the camera-ready version of the paper (should it be accepted). Since we are constrained by the 4-page limit for short paper submissions, we will attempt to do this by economising in other areas of the paper and expanding more on architectural details, first and foremost those recommended by the reviewer. We will also make sure to highlight that Lm and Ls are the same for this task. Ls and Lm are used in [12], but since they are always the same in our case, we will just use one in the camera-ready version.

2. Concerning the noise tracks used in the training and test sets, these are of different types, as well as being mixed at different SNRs. The test set is the same as that used in [9, 14, 15], we have added more information for the camera-ready version of the paper.

3. Unfortunately, with the experimental setup, we did not have enough time and computing power to perform a full grid search over hyper-parameters before the submission deadline. Our results are "hot off the press", so to speak. However, we have recently been granted access to greater computing power and are currently performing experiments under different hyper-parameter configurations, which will be included in the camera-ready version. We are producing more results including further comparisons of number of layers and different combinations of filter sizes in the downsampling and upsampling blocks.

4. Regarding the Table 2 results, this is due to the same reason as explained above - a lack of time and computing power. However, as described above, we are producing results with further experimentation on layer sizes. More specifically, these are the 10-layer (which the reviewer rightly states as being missing from the paper (due to the training cycle being incomplete at the submission deadline)) and an 8-layer model (since the 9-layer, the smallest, wins on the majority of evaluation metrics). Moreover, with this increased availability of computing power and the time available to us now, we are performing the additional fine-tuning on all models for each of these experiments, as well as those specified in Table 2.

5. Refs 9 and 15 have been amended in the camera-ready version to include their respective missing details.

6. The page 2 typing error has been corrected to [-1, 1] for the camera-ready version.

From: NIPS.cc/2018/Workshop/IRASL/Paper42/AnonReviewer1
title: Lack of novelty
review: Summary:
This paper takes the Wave-U-Net architecture, developed to separate singing voice from music, and applies it to the task of speech enhancement. Performances of the model are compared with other models on one speech enhancement benchmark, and a small study of the impact of the number of layers on performances is also presented.

Pros and Cons:
+ The paper is clearly written.
+ The proposed model performs better than the SOTA, which is a good point.
- The paper lacks novelty. Singing voice separation and speech enhancement are extremely similar tasks, so one can expect that a model that perform well on one task should also perform well on the other.
- Except from the small ablation study (which sees to indicate that speech enhancement is a simpler task than singing voice separation), the paper doesn't provide any new insights.

Minor Comments:
- Line 15: missing a space before '[18]'
- Figure 1: This figure is not referenced in the text.
- Line 57: I don't understand this sentence
rating: 2: Marginally below acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper42/AnonReviewer3
title: The contribution is fair and seems to show a certain performance improvements with respect to a recent data-driven algorithm (namely SEGAN) used for the same task, i.e. speech enhancement. Some technical details are missing and the experiments have not been conducted in a fully convincing way.
review: The authors propose a new method for speecha enhancement, based on the usage of the Wave-U-Net, recently proosed in the literature to solve the voice/music separation task. The approach is reasonable, even if the innovation impact is pretty limited since the authors just use something already advanced in the literature with basically no relevant technical modifications. The experimental results, carried out on a comparative fashion, and taking the SEGAN algorithm as main term of comparison, seem to prove the effectiveness of the proposed method.

These are the reviewer’s suggestions to improve the paper quality:
- The Wave-U-Net architecture should be better and more comprehensively described, even though it is something already known in the literature, since the manuscript should be self-explicative, at least for what concern the method proposed in the manuscript. For instance, in Fig.1, it is not clear what “Crop and concatenate” exactly means and which role such action plays within the neural architecture under study. Moreover, the authors should explain if and when Lm and Ls are different (assuming that they are equal in the speech enhancement problem object of this study).
- Datasets: are the noise types included in the test set also considered in the training set? Please, specify it.
- Experimental Setup: Why did the authors not perform any hyper-parameters optimization strategy to find the best neural configuration for their experiments? 
- Results: Why did the authors not perform any fine-tuning for the experiments related to Table 2 results? Which hyper-parameters have been selected of the Wave-U-Net architecture with reduced number of layers? It is weird to notice that the 10-layers case study is not included in the table, and the motivation provided by the authors for this lack is not really “sound”.
- References: Refs 9 and 15 are not complete, some details are missing.
- Page 2: “S_k belongs to [1,1]^…” seems not correct. The range should be [-1,1], as for “m”.





rating: 3: Marginally above acceptance threshold
confidence: 2: The reviewer is fairly confident that the evaluation is correct