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Peer review (iclr2020):
This paper presents a method for generating polyphonic music in a hierarchical manner. The hierarchy is inspired by the hierarchy used in scoring western music. Overall I like the idea and I think it has merit. However, the writing and exposition of the author*s methods still requires a lot of work, as it is rather difficult to follow how the different parts of the system work together. Finally, the subjective listening tests report a like ratio of 17.7% for the model*s pieces, compared with a 40.5% ratio for pieces composed by humans. Although I appreciate their honesty and wouldn*t expect the model to outperform trained human composers, it would be good to find a better metric to measure the model*s performance. The audio samples provided seem reasonable, but if one were to only look at the like ratio, there*s no indication that the model produces anything reasonable. Some more detailed comments below: - In the abstract you say *capable of generating long polyphonic music given number of measures, up to hundreds of measures.* What does this mean? Are these measures given to the model to condition it, or that the model can generate hundreds of measures? - First paragraph of background: *if all conditional distributions are compatible*. What does it mean to be compatible? - In Figure 2, it would be useful to have the RHS as a concrete example relative to the score on the LHS. - Top of page 4: *c is the input to the decoder*: what does this c represent? what are its dimensions? - Pargraph on page 4 starting with *All decoders are autoregressive models:* is not at all clear. *lower level* is used five times, but it*s not clear what it means. - Sec. 3.1.1: Does your representation have fixed quantization of measures? Otherwise, why do you need the ties? - All of sec 3.1.1 is very hard to follow. A diagram would be useful. - Duration encoder: *every possible digit*: it*s not clear what you mean by digit. - Duration encoder: *all digit embeddings for all prime bases*: what does *all digit embeddings* mean? seems like a really large number. - Duration decoder: What do you mean by *we consider the dependency between these digits*? - Sec. 3.1.3: *a sequence with each step*: what does *step* mean in this context? - Within-beat position signal as extra input: *whose value is wrapped to [0, 1)*: what does this mean exactly? Can we have two notes occurring at distinct times within the same measure, yet still map to the same b_i? Why is that desirable? - Cross-beat note splitting: *a note crosses multiple beats, which bypasses circles of the positional signal.* It*s not clear what this means. - All of the *Voice encoder* and *Voice decoder* sections are really hard to follow, again, a diagram would help a lot. Some issues: * what are the Ws? they*ve never been introduced! * *after summing up memory matrices...context get aggregated*: where is this happening? what do you mean by synchronous measures? multiple voices? * Again, it*s not clear what c is, nor what M is. * What*s LayerNorm? * *implements the last term on the RHS of the equation 9*: how? * *chord decoder which implements the first term on the RHS of the equation 9* how? - Sec 3.2: *then the model attends back into the memory matrix*: what does this mean? - Sec 4.1: *Our dataset has 893 pieces*: how many total measures? - Sec 4.2: You used letters to refer to many of the hyperparams discussed here, please use the same letters here. Minor corrections to improve the writing: - Abstract: s/we construct a inter-measure/we construct an inter-measure/ - *Music, largely, is a form of art with no tangible existence* is a weird way to start the paper... - Intro: s/more effortless/less effort/ - Bottom of page 1: s/However, as the rhythm becoming more complex/However, as the rhythm becomes more complex/ - Below Fig 1: The refernce *(ran, 2004)* seems incorrect, it doesn*t appear in the bibliography. - *instead of a simple word embedding lookup*, they*re not really simple... - Top of page 4: s/it is omitted from the our model/it is omitted from our model/ - Sec 3.1.1: s/a chord is a set of pitch-tie tuple,/a chord is a set of pitch-tie tuples/ - Duration encoder: s/we directly encodes/we directly encode/ - Cross-beat note splitting: s/making human musicians easy to count/making it easier for human musicians to count the beats/ - Sec. 3.2: s/representation, a plenty of neural/representation, plenty of neural/ - Sec 4.2: s/The final loss is then a average/The final loss is then the average/ - Sec 4.3: *with measure length 32, 64, and 128, respectively*: you don*t need *respectively* here. - In Fig. 4, the label *Figure 4* is missing. Also, place the legend in a space where it*s not blocking the important pieces of the plot (in fig (b) in particular).