You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: where e T , e N , S , and N denote the time-level embed- ding vectors, note-level embedding vectors, STHarm, and the number of melody notes, respectively, Embedding and Self-AttBlocks denote the embedding layer and L multi- head self-attention blocks that are identical to the vanilla Transformer, respectively [12], w ∗ denotes a sinusoidal posi- tional embedding scaled by a trainable weight [40], and TimeToNote is a novel method that we propose to convert the timewise embedding to the notewise embedding to capture the note patterns in a melody. Nevertheless, these LSTM-based studies had limitations in generating concrete chord structures. First, the models were unable to encode an original melodic structure despite their sequential architectures [4]. The notes in a melody were aggregated within a chord duration into a pitch-class histogram before being fed to the model. Second, the models did not explicitly consider capturing the patterns of chord pro- gressions. Chord labels correspond to the constant time grids (e.g., a bar or half-bar). Sequential modeling of grid-based chord labels is likely to result in ambiguous patterns or hier- archies of the generated outputs [8].

Question: What is the benefit of using note-based representation over grid-based representations?
Note-based representation is better than grid-based representation in learning chord patterns. The reason is that learning with the grid-based representation can result in generating chord progression with ambiguous patterns or hierarchies. On the other hand, modeling the note-based representation can capture note patterns in a melody.