Abstract
We have continued our work in the field of AI-driven music synthesis and have improved upon our previous 3-layer gated recurrent unit neural network, with results confirming higher accuracy and much smaller validation loss. In order to achieve this, we have designed a recurrent neural architecture that is more suited to learning the musical style of J. S. Bach from an enhanced database of partitas and sonatas. This architecture is based on four independent channels, each having two gated-recurrent units, with a bi-directional long short-term memory unit in between. However, the main incentive of this paper is finding a metric which is able to measure on a normalized scale the similarity between an artificial musical composition and the style of the famous composer. This measure is heavily based on a signal processing technique known as symbolic aggregate approximation. As a final note, we perform a statistical analysis of the recurring motifs in the works of J. S. Bach, and hint on the possibility of exploiting them to further increase the quality of the output.
Citare
A. Marinescu, A musical similarity metric based on Symbolic Aggregate Approximation, SoftCOM 2020, 28th International Conference on Software, Telecommunications and Computer Networks