reviews: 
Comments: This paper seeks to deepen the understanding of processing long sequences by means of independent processing of sub-sequences, which are taken as overlapping or non-overlapping chunks of the original sequence. After a short discussion concerning the use of chunked subsequences for sequence modeling, the paper takes the sepformer architecture as an example and demonstrates that by means of reducing the overlap processing time can be reduced. When considering a constant computational budget, the reduced computational costs may be invested into increasing the complexity of the model architecture, which finally leads to an improvement in the overall performance.
Strengths: - The paper investigates the interesting question concerning the effect of overlap for transformer-based separation models, which treat the sequence of chunks independently. 
- The most central claim of the paper: that a reduction of the overlap between subsequent chunks leads to a reduction in computational costs, and that in a situation of overall constant costs, this allows increasing the complexity of the model leaving to an overall improved performance has been experimentally demonstrated.
Weaknesses: **Context**

The processing of chunked segments with overlap is a very old signal-processing strategy. For filtering and convolutional operations, this is called  Overlap-add processing. The approach has a long history, is conceptually simple, and theoretically very well understood. See https://en.wikipedia.org/wiki/Overlap-add_method with references in there (notably Oppenheim, Alan V.; Schafer, Ronald W. (1975). Digital signal processing. Englewood Cliffs, N.J.: Prentice-Hall). Looking into the discussion on Wikipedia one can note that there is the notion of border effects, which determine the theoretically optimal length of the overlap. The longer the filter response the longer the overlap has to be, if one wants to transparently replace the processing of the full sequence by means of the chunked processing. For autoregressive filters the response duration is infinite and a compromise between memory requirements and approximation has to be found. Note that the most efficient processing will remove all overlap. Unfortunately, this will introduce an incoherence at the edges, which in turn will be heard as annoying perceptual artifacts (clicks). That's why zero overlap is hardly ever used anywhere. In case one uses segment-wise processing with overlap-add the fundamental strategy to avoid border effects is cross-fading (see for example here Fröjd, Martin, and Andrew Horner. "Sound texture synthesis using an overlap–add/granular synthesis approach." Journal of the Audio Engineering Society 57.1/2 (2009): 29-37. On google you find numerous web pages for audio practitioners)  

While  DNN (and transformers) are nonlinear the situation remains very similar. Two segments that are independently processed will be inconsistent at the edges. The deviation relates to the error the networks make compared to the optimal solution. These errors will, in most cases, be perceived as clicks. Clicks are one of the most annoying artifacts, and therefore overlap-add with cross-fades is perceptually nearly always better than not using overlap at all.  Before closing these fundamental comments, I'd like to note that there is an additional twist for DNN (including transformers). Given two separate blocks will lead to different results, and given the errors will often not be strongly correlated the cross-fade, or more generally averaging the different solutions, will lead to a slight advantage, similar to what is achieved when using ensemble algorithms (again see Wikipedia and references in there). For a practical example in the context of music source separation please see the shift trick in the first Demucs paper (https://arxiv.org/pdf/1911.13254). By means of extreme overlap, basically treating each segment 10 times, they achieve an SDR reduction of 0.3dB. Note this is not meant to say that this is the most efficient way to reduce the SDR. It is meant to underline that all these relations are very well known.
(I'd like to note that I am not involved in any of the references given.)

**Comments**

- The major part of the present paper, notably the introduction, discusses well-known facts (see the summary in the context section) that are widely used, not only in the signal-processing community but also in the machine-learning community. 

- These well-known facts are presented in a way that contains numerous problematic descriptions and even errors:
  - down-sampling is a well-established procedure consisting of regularly dropping samples. The question related to what extent down-sampling is transparent and can be reversed is theoretically well understood (see https://en.wikipedia.org/wiki/Nyquist-Shannon_sampling_theorem and references in there). 
  - Chunked processing without overlap is not down-sampling in the same way as described before. While the sample rate becomes lower (the frame rate in DSP terms), the sequence does not necessarily lose all context. The operation can be performed as a simple reshape which can be inverted. The question is how the model processes these segments. The context across the signal chunks is missing only, if the case where the segments are processed independently of each other. The context within the chunk is present in any case. When the DNN sees the sequence of chunks (as is the case for most models that work on STFT frames) then no context is missing, the context is just presented differently.  Note that even in case of overlap the context at the frame borders is still missing, but due to redundancy (each border is treated twice) and the uncorrelated errors (see shift effect above) this missing context can be partly compensated for.  
  - While it is correct that wav2vec produces features with a reduced sample rate, wav2vec is not *down-sampling* either. The latent space that is produced by wav2vec does not consist of samples, it consists of features that do not aim to represent the full signal. Comparing these two procedures is highly misleading and would require clarification.
- In line 38 authors write: For other problem areas, mainly those which output not a class or text but audio itself, downsampling
that aggressively is not possible. These problem areas include audio generation, speech synthesis, source separation, speech enhancement and audio super resolution. 

Processing of voice/music signals in the form of compressed representations (Encodec, DAC) is a very active research topic. While the representation has a much lower sample rate (so following the presentation in the current paper it qualifies as down-sampled), it does not necessarily have a negative impact and there is no problem of missing context. This is because the latent codes are not processed independently but as a sequence. The current research is a direct contradiction to the statement above.

- The presentation of the STFT is wrong.

*Line 158: The Fourier transform assumes that the signal is periodic. * 

It is the Fourier Series that assumes the signal is periodic. The Fourier transform assumes the signal square integrable (https://en.wikipedia.org/wiki/Fourier_transform)

The STFT is not used because the signals to be analyzed are not periodic, but because most interesting signals (voice) are produced by time-varying systems, to observe the evolution of the frequency characteristics over time one has to cut into small pieces.

*line 160 : To minimize spectral leakage, the signal is multiplied with a window function whose
purpose it is to taper off the start and end of the signal to 0 making the signal somewhat periodic.*

No this is not to make the signal somewhat periodic. If you apply a time-limited window function the result remains non-periodic. Note as well that spectral leakage is introduced due to the cutting into frames. Cutting out signal frames without tapering is equivalent to using a rectangular window, which creates the most dramatic leakage. The tapering changes the way the spectral leakage will take place. For details on how various analysis windows modify spectral leakage see https://en.wikipedia.org/wiki/Window_function)

- The evaluation is insufficient to evaluate the results. As I have tried to explain in the introducing remarks for zero overlap it is likely that the inconsistencies at the segment borders lead to perceptually annoying artifacts (clicks). It may very well be that the perceptual comparison between 50% overlap and 0% overlap, together with a constant computational budget would result in the 0% variant coming out better (From my experience with this kind of algorithm, I would be astonished though). In any case, simply comparing objective measures is not sufficient to draw a conclusion.
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 1
Clarity: 1