Kati8/ICLR_2023_2025 huggingface dataset:
authors: Joel Rixen
authors: Matthias Renz
reviews: 
Comments: This paper proposes a group of techniques to avoid overlap when processing long audio sequences in chunks, including shifting the sequence, using variable chunk sizes and increasing model parameters, thereby reducing computational complexity. The author conduct experiments on speech seperation and audio super-resolution to validate the effectiveness of their approach.
Strengths: Experiments on speech seperation and audio super-resolution proves that the proposed tricks do reduce computational complexity in long audio processing.
Weaknesses: 1. Regarding the increase in model size leading to performance improvements as a significant contribution seems to be not persuative, since it's more like a common sence that a larger parameter count enables the model to capture more complex probability distributions, thereby enhancing its performance. 

2. Did the authors investigate previous works related to reducing computational overhead in processing long audio sequences? This aspect is not reflected in the text. I suggest adding an extra chapter of related works, and describing how do the proposed methods compare to earlier work in terms of advantages or differences

3. No enough ablation studies are provided to validate the effectiveness of the proposed three methods. Does each of them contribute to performance improvements, or is it solely the increase in model parameter count that proves beneficial? I suggest that the authors add experiments that introduce shifting the sequence and using variable chunk sizes separately, while keeping the model architecture and parameter count unchanged. They may also increase the model parameter count without incorporating these methods and report the results to validate the effectiveness of each technique.

4. The paper seems to be not well organized, and the narrative logic is somehow disorganized.
   
   4.1. The first chapter extensively discusses overlap instead of providing an introduction or overview of the whole paper. I suggest the authors to use the first chapter to briefly outline the background of the problem, the proposed methods, and their contributions (i.e., an overview of the entire paper). Following this, a separate chapter could be added to analyze the overlap issue in segmenting long audio sequences.

   4.2. In section three, the authors use lengthy descriptions on the modifications to the speech separation model, while the architecture illustrations are overly brief, providing only a sequence of modules without detailing each one, which makes it difficult for readers to understand the design. The authors could consider replacing lengthy descriptive text with more detailed architectural diagrams to enhance clarity.
Recommendation: 3
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 1
reviews: 
Comments: Sequence modeling techniques such as Transformers scale quadratically with input sequence lengths so it is not viable to apply transformers to long speech sequences. The long inputs are split into equal-sized chunks and then sequence modeling is applied to much shorter chunks. To avoid losing the context that exists between chunks, the sequence is divided into overlapping chunks instead. However, this duplicates the information that exists between chunks and increases the computational cost. 

The paper proposes an alternative to overlapped chunks for processing long speech sequences. The paper describes two strategies for removing or reducing overlaps:  shifting the sequence and using different window sizes. By removing the overlap between the chunks, the models effectively process less data and this significantly improves computation time and reduces memory usage.
Strengths: The paper is unique and interesting. It explores alternatives to commonly accepted overlapped chunking for long speech sequences. The proposed techniques achieve a significant reduction in runtime and memory usage for two different tasks: speech separation and audio super-resolution.
Weaknesses: The writing and the organization of the paper can be improved. The paper feels bloated in a few places e.g. there is no need for Figure 1,2 to be almost full pages. There is no need for five examples of chunking. At the same time, some experimental details are missing. For example, "We increase the shifts by 10 for each Transformer starting from 0. In our experiments changing the shift value had only minor impact on accuracy as long as it was not too small in reference to the chunk size." 

What is too small? At what point does the shift value not impact the final performance? What is the final chunk size used in the experiments?
Including a graph can help answer these questions. The experimental section would benefit from ablation experiments concerning different adjustments made to the modified models.
Recommendation: 5
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 2