of paper
On the plus side: This paper is well written, the experiments are fairly well done, the results are good, and the outlined approach is sensible.
On the minus side: It isn't clear what the scientific contribution is. Using a known network structure on a known feature to perform a known process doesn't really provide much insight. I do feel that there is room for insight in this paper, but the authors stick to a very high-level description of their experiments. Recommendation
I would recommend rejection. I think this is good work, but it doesn't convey to me anything that I didn't already know (other than this particular combination of feature and network seems to work well). I expect papers to teach me something, not just report a finding without any analysis or explanation. As is, the paper is really not much more than what is simply contained in table 1. This paper could have in it a lot more information and analysis to make it a much stronger submission, but the current approach is simply a report on a single point estimate. Yes it seems to work well, but I don't believe that just that is the standard for a strong publication.
Questions for clarifications:
• A hugely influential factor in multi-source localization is how one defines a peak. This is not described adequately in this paper and I think needs some clarification. I understand that for each time frame we obtain a location posterior. But it isn't clear how to a) Find out how many speakers are active, and b) finding out the peaks that correspond to each speaker. If we have e.g. two speakers and six substantial nonzero posterior values, how does one pick the two that correspond to a speaker? We cannot use the two loudest ones, they might be both from the same speaker. What if the amplitude difference between the speakers is large? This is not a question that can be brushed away so easily and should be directly addressed.
• It would be interesting to see something akin to a confusion matrix. Arrays are not equally good at detecting all directions and understanding how this algorithm behaves with respect to classic array behavior would be an interesting bit of information.
Provide additional feedback with the aim to improve the paper
• I wouldn't use the terms "spectral and phase". The distinction should be "magnitude and phase"; "spectral" usually implies the whole complex-valued output of the DFT. But that entire sentence is a little out of place, since you claim that phase is more useful than magnitudes, and then you use the complex data directly. I would simply state which features you use and leave it at that. The extra discussion there is an unnecessary distraction.
• "since it is known to encapsulate the spatial fingerprint for each sound source", I assume when you say source you imply a spatial location. I believe "source" tends to be usually interpreted as the actual sound (hence "source separation"), which is independent of the location.
• Your VAD description is puzzling. What is stated in the paper simply discards any TF bins that have a magnitude of less than epsilon. If this is what you do I wouldn't call it a VAD, you are simply discarding TF bins with zero magnitude that will result in a division by zero. A VAD is supposed to look for the presence of speech (not just energy), and is also very unlikely to be defined over frequency, it's usually only over time.
• I understand that for the sake of conforming to current APIs, complex numbers are being avoided here. But, for something on paper, it strikes me as quite strange to stack real and imaginary parts when a complex-valued representation is clearly much easier to describe and manipulate. I can predict your rebuttal to this point, but papers are supposed to set forth the science from which we will write code, and not directly describe the code.
• Your VAD description is puzzling. What is stated in the paper simply discards any TF bins that have a magnitude of less than epsilon. If this is what you do I wouldn't call it a VAD, you are simply discarding TF bins with zero magnitude that will result in a division by zero. A VAD is supposed to look for the presence of speech (not just energy), and is also very unlikely to be defined over frequency, it's usually only over time.