6533b7d3fe1ef96bd126021c
RESEARCH PRODUCT
Simulating music with associative self-organizing maps
Antonio ChellaMagnus JohnssonHaris DindoMiriam Buonamentesubject
MelodySelf-organizing mapComputer scienceCognitive NeuroscienceExperimental and Cognitive PsychologyContext (language use)02 engineering and technologycomputer.software_genre050105 experimental psychologyArtificial Intelligence0202 electrical engineering electronic engineering information engineering0501 psychology and cognitive sciencesInternal simulationArchitectureAssociative propertySettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionibusiness.industry05 social sciencesInformation and Computer ScienceNeural networkAssociative self-organizing map020201 artificial intelligence & image processingArtificial intelligencebusinesscomputerMusicNatural language processingdescription
Abstract We present an architecture able to recognise pitches and to internally simulate likely continuations of partially heard melodies. Our architecture consists of a novel version of the Associative Self-Organizing Map (A-SOM) with generalized ancillary connections. We tested the performance of our architecture with melodies from a publicly available database containing 370 Bach chorale melodies. The results showed that the architecture could learn to represent and perfectly simulate the remaining 20% of three different interrupted melodies when using a context length of 8 centres of activity in the A-SOM. These promising and encouraging results show that our architecture offers something more than what has previously been proposed in the literature. Thanks to the inherent properties of the A-SOM, our architecture does not predict the most likely next pitch only, but rather continues to elicit activity patterns corresponding to the remaining parts of interrupted melodies by internal simulation.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-08-01 | Biologically Inspired Cognitive Architectures |