Research on automatic music generation lacks consideration of the originality of musical outputs, creating risks of plagiarism and/or copyright infringement. We present the originality report – a set of analyses for measuring the extent to which an algorithm copies from the input music on which it is trained. First, a baseline is constructed, determining the extent to which human composers borrow from themselves and each other in some existing music corpus. Second, we apply a similar analysis to musical outputs of runs of MAIA Markov and Music Transformer generation algorithms, and compare the results to the baseline. Third, we investigate how originality varies as a function of Transformer’s training epoch. Results from the second analysis indicate that the originality of Transformer’s output is below the 95%-confidence interval of the baseline. Musicological interpretation of the analyses shows that the Transformer model obtained via the conventional stopping criteria produces single-note repetition patterns, resulting in outputs of low quality and originality, while in later training epochs, the model tends to overfit, producing copies of excerpts of input pieces. We recommend the originality report as a new means of evaluating algorithm training processes and outputs in future, and question the reported success of language-based deep learning models for music generation. Supporting materials (code, dataset) will be made available via https://osf.io/96emr/.
|Title of host publication||Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021|
|Number of pages||16|
|Publication status||Published - 2 Apr 2021|
|Name||Lecture Notes in Computer Science|
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