Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper presents a convLSTM based audio frame prediction approach as a method of unsupervised learning of representative audio features.  Proposed model is  trained using a combination of mean squared error as well as a pair wise similarity measure.  Model and the training approach are evaluated on the task of audio event classification.

While the combination of the ideas is novel, the individual elements model and training approach are previously known.  It is also not intuitively clear why a predictive auto-encoding would be a good unsupervised feature learning approach for the task of audio event classification, thus I’d like to see comparisons with some well known basic unsupervised feature learning approaches (e.g. VAE, ladder networks, etc.).

Results are presented on an audio event detection dataset which is relatively new and not many reference comparisons are available.  To make the paper stronger I’d also advise authors provide comparisons with other known results on this task, and also to apply their feature learning approach to other well established sound classification tasks (e.g. phone classification in TIMIT).

Overall I feel the paper is not strong enough in current shape for ICLR.
review_title: null

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper combines several ideas, ConvLSTM autoencoders and a pairwise lose. The idea is to do sound classification/clustering.

I feel this paper is more suited towards the signal processing community (i.e., ICASSP/INTERSPEECH). The main problem I have with this paper/task it seems too specific and there isn't enough core-ML contributions for this round of ICLR workshop acceptance. Sequence autoencoders (see Dai et al.,) and ConvLSTM (as cited by authors Zhang et al.,) and pair wise losses (see SIGIR) are not new. Merging all these ideas together is a contribution, but I am not sure it would generate a lot of interest in the ICLR community.

Note:
This reviewer is unfamiliar w/ the "acoustic event dataset (AED) from Takahashi et al. (2016)" used in evaluation.

Citations Missing:
https://arxiv.org/pdf/1511.01432.pdf (for sequence autoencoders which this model is quite similar).
review_title: review

Decision: Accept