Transformer-XL Based Music Generation with Multiple Sequences of Time-valued Notes
This work addresses the problem of improving AI-based classical music generation for creators and researchers, offering incremental advancements over existing methods.
The paper tackles the problem of generating classical music by addressing limitations in single-sequence note representation and tempo-dependent note values, proposing a framework with time-valued note sequences and four separate sequences for training Transformer-XL networks. The result is significantly better and hour-level longer music generation compared to state-of-the-art baselines, as demonstrated on a 23-hour piano MIDI dataset.
Current state-of-the-art AI based classical music creation algorithms such as Music Transformer are trained by employing single sequence of notes with time-shifts. The major drawback of absolute time interval expression is the difficulty of similarity computing of notes that share the same note value yet different tempos, in one or among MIDI files. In addition, the usage of single sequence restricts the model to separately and effectively learn music information such as harmony and rhythm. In this paper, we propose a framework with two novel methods to respectively track these two shortages, one is the construction of time-valued note sequences that liberate note values from tempos and the other is the separated usage of four sequences, namely, former note on to current note on, note on to note off, pitch, and velocity, for jointly training of four Transformer-XL networks. Through training on a 23-hour piano MIDI dataset, our framework generates significantly better and hour-level longer music than three state-of-the-art baselines, namely Music Transformer, DeepJ, and single sequence-based Transformer-XL, evaluated automatically and manually.