Venue: ICLR 2017 conference

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: While I understand the difficulty of collecting audio data from animals, I think this type of feature engineering does not go in the right direction. I would rather see a model than learns the feature representation from data.  I would think it should be possible to collect a more substantial corpus in zoos / nature etc, and then train a generative model. The underlying learned feature representation could be then used to feed a classifier. I'm not familiar with the particularities of this task, it's hard to judge the improvements by using chirplets.
review_title: Chirplet transforms for small data tasks

final_score: rating: null
review_content: First, few typos:
2.2: " A wavelet is an atom with compact support in time and frequency domain which integrates to 0"
You mean "often localized". (Haar wavelets are not and the compact support in fourier and spatial domain is mathematically not possible)

2.2: Otherwise, few remarks: The wavelet should be normalized. There should be a downsampling on Ux(lambda,t). The phase is not an information here, it’s a variability since it corresponds to a local translation.


I would have a few questions:

It seems that you are using first order coefficients of scattering. Why so? The representation could be potentially more discriminative with higher order coefficients. Do you have some suggestions of operators that could be used to get second order coefficients? Did you try to remove the low-pass filtering? Indeed, your representation might lose discriminative information because of this averaging. 

Have you done a comparison of your pipeline with a translation scattering transform or a joined scattering transform( https://arxiv.org/pdf/1512.02125.pdf )? It is claimed that in the case of chirp(and especially birds), the representation is more sparse.

What is the Littlewood-paley of your representation? Before and after averaging, is the representation sparse?

Did you apply a logarithm on the Chirplet coefficients, with the CNN+Chirplet? This dataset seems to have a lot of noise, have you tried to use a threshold?

How did you chose the parameters to create your chirplet filters? Are they standard or cross-validated? Do you think you will have to adapt them when using a new dataset?(e.g. are they generic?)

What is the final size of the representation of a signal of size T? I think there is 110 channels, but it is not clear to me!

How did you design the Audio2Chirp CNN, described in Appendix B? Could you initialize the filters with the Chirplet transform?

Thank you!

review_title: Design of the Chirplets

final_score: rating: 6: Marginally above acceptance threshold
review_content: Pros: 
- Introduction of a nice filter banks and its implementation
- Good numerical results
- Refinement of the representation via back propagation, and a demonstration that it speeds up learning

Cons:
- The algorithms (section 3.1) are not necessary, and they even affect the presentation of the paper. However, a source code would be great!
- The link with a scattering transform is not clear
- Sometimes (as mentionned in some of my comments), the writing could be improved.

From a personal point of view, I also believe the negative points I mention can be easily removed.
review_title: A work to try to structure a deep network

final_score: rating: 4: Ok but not good enough - rejection
review_content: The authors advocate use of chirplets as a basis for modeling audio signals.  They introduce a fast chiplet transform for efficient computation. Also introduced is the idea of initializing (pre-training) CNN layers to mimic chirplet transform of audio signal (similar to ideas proposed by Mallet et al. on scattering transforms).  The paper is fairly easy to follow but in a few places contains undefined terms (e.g. AM-FM, MAP).

While the idea of using chirplet transform is interesting, my main concern is that the empirical evidence provided is in a rather narrow domain of bird call classification.  Furthermore, the accuracy gains shown in that domain are relatively small (61% MAP for log-Mel features vs 61.5% for chirplet transforms).  I would recommend that authors provide evidence for how this generalizes to other audio (including speech) tasks.
review_title: null

Meta-review: This paper studies efficient signal representations to perform bioacoustic classification based on CNNs. Contrary to image classification, where most useful information can be extracted with spatially localized kernels, bioacoustic signatures are more localized in the frequency domain, requiring to rethink the design of convolutional architectures. The authors propose to enforce the lower layers of the architecture with chirplet transforms, which are localized in the time-frequency plane as wavelets, but with time-varying central frequency. They present preliminary numerical experiments showing promising improvements over existing baselines. 
 
 The reviewers found interest in the method, but raised concerns on the relatively narrow scope of the method, as well as the clarity and rigor of the presentation. Whereas the first concern is up to debate, I agree that the paper currently suffers from poor english which affects its clarity. 
 
 Despite these concerns, the AC finds the contribution useful in the broader context of inductive bias and injecting priors in neural networks. This is an example where the inductive priors that work well on images (localized convolutions rather than generic fully connected layers) are not sufficient unless given massive amounts of data. The AC thus recommends rejection, but invites the contribution to the workshop track.
Decision: Invite to Workshop Track