Paper:
We consider the problem of learning high-level controls over the global structure of sequence generation, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer autoencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global embedding with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and and melody. Empirically, we demonstrate the effectiveness of our method on a variety of music generation tasks on the MAESTRO dataset and an internal dataset with 10,000+ hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to relevant baselines.

Review:
This paper presents a technique for encoding the high level “style” of pieces of symbolic music. The music is represented as a variant of the MIDI format. The main strategy is to condition a Music Transformer architecture on this global “style embedding”.  Additionally, the Music Transformer model is also conditioned on a combination of both “style” and “melody” embeddings to try and generate music “similar” to the conditioning melody but in the style of the performance embedding. 
Overall, I think the paper presents an interesting application and parts of it are well written, however I have concerns with the technical presentation in parts of the paper and some of the methodology. Firstly, I think the algorithmic novelty in the paper is fairly limited. The performance conditioning vector is generated by an additional encoding transformer, compared to the Music Transformer paper (Huang et. al. 2019b). However, the limited algorithmic novelty is not the main concern. The authors also mention an internal dataset of music audio and transcriptions, which can be a major contribution to the music information retrieval (MIR) community. However it is not clear if this dataset will be publicly released or is only for internal experiments. 
In terms of technical presentation, I think the authors should clarify how the model is trained. It took me a couple of passes and reading the Music Transformer paper to realise that in the melody and performance conditioning case, the aim is to generate the full score (melody and accompaniment) while conditioning on the performance style and melody (which is represented using a different vocabulary). This point can be easily clarified in Figure 1, by adding the input to the encoder as input to the decoder for computing the loss. Although I understand the need for anonymity and constraints while referring to unreleased datasets, it would still be useful for the reader/reviewer to have some details of how the melody was extracted and represented. “An internal procedure” is quite mysterious. 
Measuring music similarity is a difficult problem and the topic has been the subject of at least 2 decades of research. I find the description of the “performance feature” to be lacking in necessary background and detail. Firstly, I am not sure what the final dimensionality of the feature vector is. Is it real valued? The authors mention (Yang and Lerch, 2018) but use a totally different set of attributes compared to that paper. I also don’t see the connection between this proposed feature vector and using the IMQ kernel for measuring similarity. This connection is not motivated adequately and after reading (Jitkrittum et. al. 2019) its not obvious to me why this is the most appropriate metric. Finally, it would be useful if the authors comment on existing methods for measuring music similarity in symbolic music and how their proposed feature fits into existing work. A lot of work has been published on this topic, most recently in the context of Query-by-Humming [1]. 
Minor Comments
1. “...which typically incorporate global conditioning as part pf the training procedure” Could you elaborate on this point? Is the global conditioning the samples from the noise distribution? 
2. Figure 1 should be clarified or another figure should be added to show how the melody conditioning works. Maybe a comment on the melody vocabulary or a reference would also be useful. 
3. The MAESTRO dataset is described in terms of the number of performances while the internal dataset is described in terms of the number of hours of audio. Its not possible for the reader to get a sense of the relative sizes of the 2 datasets and how the results should be interpreted. 
4. There should be more background and description in Section 4. Where does the performance feature come from? Why use this feature compared to existing techniques for measuring similarity between symbolic music pieces? Is it computational efficiency? Why not compare the conditioning melody with the generated performance similar to query-by-humming? Where does the IMQ kernel come from? What is the size of the feature vector? 
5. In section 5.2, a conditioning sample, a generated sequence and an unconditional sample are used to compute the similarity measure. Which terms do these correspond to in the MMD-like term (x,y,y’)? 
6. I like the experiments performed in Section 5.3 with the linear combination of 2 performance embeddings. 
[1] A Survey of Query-By-Humming Similarity Methods: http://vlm1.uta.edu/~athitsos/publications/kotsifakos_petra2012.pdf