A Transformer Based Pitch Sequence Autoencoder with MIDI Augmentation
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Algorithms based on deep learning have been widely put forward for automatic music generated. However, few objective approaches have been proposed to assess whether a melody was created by automatons or Homo sapiens. Conference of Sound and Music Technology (2020) provides us a great opportunity to cope with the problem. In this paper, a masked language model based on ALBERT trained with AI-composed single-track MIDI is demonstrated for composers classification tasks. Besides, music tune transposition and MIDI sequence truncation is applied for data augments. To prevent from over-fitting, a refined loss function is proposed and the amount of parameters is reduced. This work provides a new approach to tackle the problem on obtaining features from tiny dataset which is common in music signal analysis and deserve more attention.
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