SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation task that generates one audio sample at a time. We show that our model which profits from combining memory-less modules, namely autoregressive multilayer perceptron, and stateful recurrent neural networks in a hierarchical structure is de facto powerful to capture the underlying sources of variations in temporal domain for very long time on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.
The paper proposed a novel SampleRNN to directly model waveform signals and achieved better performance both in terms of objective test NLL and subjective A/B tests. As mentioned in the discussions, the current status of the paper lack plenty of details in describing their model. Hopefully, this will be addressed in the final version. The authors attempted to compare with wavenet model, but they didn*t manage to get a model better than the baseline LSTM-RNN, which makes all the comparisons to wavenets less convincing. Hence, instead of wasting time and space comparing to wavenet, detailing the proposed model would be better.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation task that generates one audio sample at a time. We show that our model which profits from combining memory-less modules, namely autoregressive multilayer perceptron, and stateful recurrent neural networks in a hierarchical structure is de facto powerful to capture the underlying sources of variations in temporal domain for very long time on three datasets of different nature. Human evaluation on the generated samples indicate that our model is preferred over competing models. We also show how each component of the model contributes to the exhibited performance.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposed a novel SampleRNN to directly model waveform signals and achieved better performance both in terms of objective test NLL and subjective A/B tests. As mentioned in the discussions, the current status of the paper lack plenty of details in describing their model. Hopefully, this will be addressed in the final version. The authors attempted to compare with wavenet model, but they didn*t manage to get a model better than the baseline LSTM-RNN, which makes all the comparisons to wavenets less convincing. Hence, instead of wasting time and space comparing to wavenet, detailing the proposed model would be better.
assistant
The paper proposed a novel SampleRNN to directly model waveform signals and achieved better performance both in terms of objective test NLL and subjective A/B tests. As mentioned in the discussions, the current status of the paper lack plenty of details in describing their model. Hopefully, this will be addressed in the final version. The authors attempted to compare with wavenet model, but they didn*t manage to get a model better than the baseline LSTM-RNN, which makes all the comparisons to wavenets less convincing. Hence, instead of wasting time and space comparing to wavenet, detailing the proposed model would be better.