From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper31/Authors
title: NIPS Spatiotemporal Worksop 2018 - Registration for NIPS
comment: Dear Program Chairs,

Thank you for accepting our submission. We are thrilled to be a part of the NIPS Spatiotemporal Workshop 2018. Since one of us would require a Canadian visa to attend the workshop, it would be beneficial if we could register earlier so that we would have time to apply for the visa. Kindly let us know if this would be possible,

Thanking you in advance

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper31/AnonReviewer2
relevance: 4: high
impact: 3: borderline
novelty: 3: borderline

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper31/AnonReviewer2
evaluation: 
The paper is tackling the traditional problem for Indian Classical Music: Classification of Raag.
The tackled problem is not simple, and previous methods have just considered a simplified problem by predefining the musical tonic. In contrast, authors manage to allow the use of any tonic. 
Authors claim to use CNNs for the first time to solve this problem which bring several possibilities for future exploration.  However, it is not possible to analyze the efficiency of their method in comparison with other state-of-art techniques as the authors just used their technique in the experiments.  It is not possible to simply consider the accuracy reported by other works  as they were tested over different datasets.

Strengthness:
- Authors considered a big glanularity of discrete pitch notes by representing the notes by Midi,Cents notation.
- Authors use the popular CNN taking advantage of its invariance to translation capabilities which is a positive point.  
- Analysis and preprocessing of raag is not a trivial tasks and authors managed to model the problem, keep the temporal factor (by sampling many pitch sequences) and asses their method.

Weaknesses:
* The authors explain the terminology and the details of the difficulty of the problem, but they fail to explain the motivation: why do we need to classify Raags? How can it be useful?
* The third point on Part 3.1.4  on Data Augmentation is confusing.
* A discussion on the reason by which authors didn’t choose other commonly used techniques in temporal domains such as LSTM or GRU would have been helpful.  By using LSTM or GRU, authors could keep track of longer temporal information which could help to prevent the prolonged note phenomenon
* Further explanation/graphic explaining the reason for choosing the parameters such as filters and kernel size would help. 
* Experiments don’t compare the proposed method with other methods from literature.

* Some plural/singular mistakes in lines:
- 80 = All recordings WERE (instead of was)
- 97 = frequencies ARE(instead of is)
- 116 = clips ARE(not is)
* line 72 states: available for viewing here (where?)

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper31/AnonReviewer1
evaluation: Topic is great but paper is poorly written!

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper31/AnonReviewer1
relevance: 4: high
impact: 4: high
novelty: 4: high

From: ~Rohitash_Chandra1
comment: The introduction of the paper is very poor, does not cite any references. The experiments section is questionable, the results are not presented well. The topic is great but the paper needs a lot of work!
title: Needs better presentation