  Pitch and meter are two fundamental music features for symbolic music
generation tasks, where researchers usually choose different encoding methods
depending on specific goals. However, the advantages and drawbacks of different
encoding methods have not been frequently discussed. This paper presents a
integrated analysis of the influence of two low-level feature, pitch and meter,
on the performance of a token-based sequential music generation model. First,
the commonly used MIDI number encoding and a less used class-octave encoding
are compared. Second, an dense intra-bar metric grid is imposed to the encoded
sequence as auxiliary features. Different complexity and resolutions of the
metric grid are compared. For complexity, the single token approach and the
multiple token approach are compared; for grid resolution, 0 (ablation), 1
(bar-level), 4 (downbeat-level) 12, (8th-triplet-level) up to 64
(64th-note-grid-level) are compared; for duration resolution, 4, 8, 12 and 16
subdivisions per beat are compared. All different encodings are tested on
separately trained Transformer-XL models for a melody generation task.
Regarding distribution similarity of several objective evaluation metrics to
the test dataset, results suggest that the class-octave encoding significantly
outperforms the taken-for-granted MIDI encoding on pitch-related metrics; finer
grids and multiple-token grids improve the rhythmic quality, but also suffer
from over-fitting at early training stage. Results display a general phenomenon
of over-fitting from two aspects, the pitch embedding space and the test loss
of the single-token grid encoding. From a practical perspective, we both
demonstrate the feasibility and raise the concern of easy over-fitting problem
of using smaller networks and lower embedding dimensions on the generation
task. The findings can also contribute to futural models in terms of feature
engineering.
