Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 3: Clear rejection
review_content: The paper shows the image recognition CNNs can be used for whale sound detection.

Pros:
* It's interesting to know the pretrained CNNs work across domains.

Cons: 
* Experiments were not well designed. No comparisons were made. 
* The frontend processing is not conventional audio processing steps, no justification was provided why the authors decided to use the presented way. Especially, FFT generated spectrogram has 2048 bins, which are converted to 256x256 pixel images, how? Is the time-frequency structure maintained? If downsampled from 2048 to 256, why not directly output 256 bin FFT?
* No discussions on why the pretrained CNNs work for this particular task. Is the performance gained by using CNNs or the pretraining on images?
* No sound detection literature was mentioned. The task of whale sound detection may be rare, but there is a huge literature of speech/voice detection, which share the similar processing framework.
review_title: Interesting results but not much contribution to the community

final_score: rating: 3: Clear rejection
review_content: This works presents a single pipeline for whale sound classification using an image classification CNN on top of spectrogram, followed by a SVM classifier.

Unfortunately the paper does not provide any baseline which would verify usefulness of the image-CNN on a spectrogram (e.g. training the SVM directly on top of the spectrogram). The use of CNN trained for image classification for audio data is rather controversial (e.g. due to different statistics and required invariances of the data) and the work does not provide any proof that it is doing anything more than a random projections.
The work uses several terms incorrectly (detection vs. classification, incorrectly assigning CNN models to "imagenet framework" etc.).

Pros:
- Interesting dataset which may be useful for future research
- Bravery to use image classification network for sound spectrogram classification

Cons:
- Lack of any simple baseline which would motivate the use of computationally expensive CNN trained for image classification
- Several technical inaccuracies in the text


review_title: Incomplete analysis for a workshop submission

Decision: Reject