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The first step to apply deep learning techniques for symbolic music
understanding is to transform musical pieces (mainly in MIDI format) into
sequences of predefined tokens like note pitch, note velocity, and chords.
Subsequently, the sequences are fed into a neural sequence model to accomplish
specific tasks. Music sequences exhibit strong correlations between adjacent
elements, making them prime candidates for N-gram techniques from Natural
Language Processing (NLP). Consider classical piano music: specific melodies
might recur throughout a piece, with subtle variations each time. In this
paper, we propose a novel method, NG-Midiformer, for understanding symbolic
music sequences that leverages the N-gram approach. Our method involves first
processing music pieces into word-like sequences with our proposed unsupervised
compoundation, followed by using our N-gram Transformer encoder, which can
effectively incorporate N-gram information to enhance the primary encoder part
for better understanding of music sequences. The pre-training process on
large-scale music datasets enables the model to thoroughly learn the N-gram
information contained within music sequences, and subsequently apply this
information for making inferences during the fine-tuning stage. Experiment on
various datasets demonstrate the effectiveness of our method and achieved
state-of-the-art performance on a series of music understanding downstream
tasks. The code and model weights will be released at
https://github.com/WouuYoauin/NG-Midiformer.