论文标题
基于变压器-XL的音乐生成具有多个时间值序列
Transformer-XL Based Music Generation with Multiple Sequences of Time-valued Notes
论文作者
论文摘要
当前基于AI的最先进的古典音乐创作算法（例如音乐变压器）是通过使用时班的单个音符序列来训练的。绝对时间间隔表达式的主要缺点是在一个或MIDI文件中或中间有共享相同的注释值但速度不同的音符相似性计算的难度。此外，单个序列的使用限制了模型分别有效地学习和谐和节奏等音乐信息。 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.通过对23小时的钢琴MIDI数据集进行培训，我们的框架比三个最先进的基线，即音乐变压器，DEEPJ和单个序列的变压器-XL，可以自动评估和手动评估。
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.
