text: Summary ------- This paper proposes a generative model of symbolic (MIDI) melody in western popular music. The model uses an LSTM architecture to map sequences of chord symbols and structural identifiers (e.g., verse or chorus) to predicted note sequences which constitute the melody line. The key innovation proposed in this work is to jointly encode note symbols along with timing and duration information to form musical "words" from which melodies are composed. The proposed model compares compared favorably to prior work in listener preference and Turing-test studies, and performs. Quality ------- Overall, I found the paper interesting, and the provided examples generated by the model sound relatively good. The quantitative evaluations seem promising, though difficult to interpret fully due to a lack of provided detail (see below). Apart from clarification issues enumerated below, where the paper could be most substantially improved is in the evaluation of the various ingredients of the model. Many ideas are presented with some abstract motivation, but there is no comparative evaluation to demonstrate what happens when any one piece is removed from the system. Some examples:- How important is the "song part" contextual input? - What happens if the duration or timing information is not encoded with the note? - How important is the pitch range regularization? Since the authors claim these ideas as novel, I would expect to see more evaluation of their independent impact on the resulting system. Without such an evaluation, it is difficult to take any general lessons away from this paper. Clarity ------- While the main ideas of this paper are presented clearly, I found the details difficult to follow. Specifically, the following points need to be substantially clarified: - The note encoding described in Section 3.1: "w_i = (p_i, t_i, l_i)" describes the pitch, timing, and duration of the i'th chord, but it is not explained how time and duration are represented. Since these are derived from MIDI, I would expect either ticks or seconds -- or maybe a tempo-normalized variant -- but Figure 2 suggests staff notation, which is not explicitly coded in MIDI. Please explain precisely how the data is represented. - Also in Section 3, several references are made to a "previous" model, but no citation is given. Was this a specific published work? - Equation 1 is missing a variable (j) for the range of the summation. It took a few passes for me to parse what was going on here. One could easily mistake it for summing over i to describe partial subsequences, but I don't think this is what is intended. - "... our model does not have to consider intervals that do not contain notes" -- this contradicts the implication of Figure 2b, where a rest is explicitly notated in the generated sequence. Since MIDI does not explicitly encode rests (they must be inferred from the absence of note events), I'd suggest wording this more carefully, and being more explicit about what is produced by the model and what is notational embellishment for expository purposes. - Equation 2 describes the LSTM gate equations, but there is no concrete description of the model architecture used in this paper. How are the hidden states mapped to note predictions? What is the loss function and optimizer? These details are necessary to facilitate replication. - Equation 3 and the accompanying text implies that song part states (x_i) are conditionally independent given the current chord state (z_i). Is that correct? If so, it seems like a strange choice, since I would expect a part state to persist across multiple chord state transitions. Please explain this part in more detail. Also a typo in the second factor: p(z_N | z_{N-1}) should be p(z_n | z_{n-1}); likewise  p(x_n | z_N). - The regularization penalty (Alg. 1) is also difficult to follow. Is S derived from  P by viterbi decoding, or independent (point-wise argmax) decoding? What exactly is the "E" that results in the derivative at step 8, and why does the derivative for p_i depend on the total sum C? This all seems non-obvious, and worth describing in more detail since it seems critical to the performance of the model. - Table 2: what does "# samples" mean in this context? Why is it different from "# songs"? - Section 4.2: the description of the evaluation suggests that the proposed model's output was always played before the baseline. Is that correct? If so, does that  bias the results? - Section 4.2: are the examples provided to listeners just the melody lines, or full mixes on top of the input chord sequence? It's unclear from the text, and it seems like a relevant detail to correctly assess the fairness of the comparison to the  baselines. - Section 4.2: how many generated examples were included in this evaluation? Should this instead be in or out of key, since the tuning is presumably fixed by MIDI synthesis? Originality ----------- As far as I know, the proposed method is novel, though strongly related to (cited) prior work. The key idea seems to be encoding of notes and properties as analogous to "words". I find this analogy a little bit of a stretch, since even with timing and duration included, it's hard to argue that a single note event has semantic content in the way that a word does. A little more development of this idea, and some more concrete motivation for the specific choices of which properties to include, would go a long way in strengthening the paper. Significance ------------ The significance of this work is difficult to assess without independent evaluation of the proposed novel components.