TY - GEN
T1 - An Application for Evolutionary Music Composition Using Autoencoders
AU - McArthur, Robert Neil
AU - Martin, Charles Patrick
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This paper presents a new interactive application that can generate music according to a user’s preferences inspired by the process of biological evolution. The application composes sets of songs that the user can choose from as a basis for the algorithm to evolve new music. By selecting preferred songs over successive generations, the application allows the user to explore an evolutionary musical space. The system combines autoencoder neural networks and evolution with human feedback to produce music. The autoencoder component is used to capture the essence of musical structure from a known sample of songs in a lower-dimensional space. Evolution is then applied over this representation to create new pieces based upon previously generated songs the user enjoys. In this research, we introduce the application design and explore and analyse the autoencoder model. The songs produced by the application are also analysed to confirm that the underlying model has the ability to create a diverse range of music. The application can be used by composers working with dynamically generated music, such as for video games and interactive media.
AB - This paper presents a new interactive application that can generate music according to a user’s preferences inspired by the process of biological evolution. The application composes sets of songs that the user can choose from as a basis for the algorithm to evolve new music. By selecting preferred songs over successive generations, the application allows the user to explore an evolutionary musical space. The system combines autoencoder neural networks and evolution with human feedback to produce music. The autoencoder component is used to capture the essence of musical structure from a known sample of songs in a lower-dimensional space. Evolution is then applied over this representation to create new pieces based upon previously generated songs the user enjoys. In this research, we introduce the application design and explore and analyse the autoencoder model. The songs produced by the application are also analysed to confirm that the underlying model has the ability to create a diverse range of music. The application can be used by composers working with dynamically generated music, such as for video games and interactive media.
KW - Algorithmic music composition
KW - Autoencoder neural networks
KW - Interactive evolutionary computation
UR - http://www.scopus.com/inward/record.url?scp=85107436634&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72914-1_29
DO - 10.1007/978-3-030-72914-1_29
M3 - Conference contribution
SN - 9783030729134
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 443
EP - 458
BT - Artificial Intelligence in Music, Sound, Art and Design - 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Proceedings
A2 - Romero, Juan
A2 - Martins, Tiago
A2 - Rodríguez-Fernández, Nereida
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021 held as Part of EvoStar 2021
Y2 - 7 April 2021 through 9 April 2021
ER -