Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation
This addresses music composition challenges for creators by offering an adaptive, hardware-based approach, though it appears incremental in applying memristor properties to this domain.
The researchers tackled music generation by using memristor networks to overcome Markov chain limitations, resulting in melodies that evolve in style over time through feedback mechanisms.
We undertook a study of the use of a memristor network for music generation, making use of the memristor's memory to go beyond the Markov hypothesis. Seed transition matrices are created and populated using memristor equations, and which are shown to generate musical melodies and change in style over time as a result of feedback into the transition matrix. The spiking properties of simple memristor networks are demonstrated and discussed with reference to applications of music making. The limitations of simulating composing memristor networks in von Neumann hardware is discussed and a hardware solution based on physical memristor properties is presented.