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Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition. / Mcloughlin, Michael; Ingram, Simon; Rendell, Luke Edward; Lamoni, Luca Ubaldo; Kirke, Alexis; Garland, Ellen Clare; Noad, Michael; Miranda, Eduardo.

Proceedings SMC 2016. ed. / Rolf Großmann; Georg Hajdu. Hochschule für Musik und Theater Hamburg, 2016. p. 274-280 (Proceedings of the SMC Conferences).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Mcloughlin, M, Ingram, S, Rendell, LE, Lamoni, LU, Kirke, A, Garland, EC, Noad, M & Miranda, E 2016, Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition. in R Großmann & G Hajdu (eds), Proceedings SMC 2016. Proceedings of the SMC Conferences, Hochschule für Musik und Theater Hamburg, pp. 274-280, 13th Sound and Music Computing Conference and Summer School, Hamburg, Germany, 31/08/16.

APA

Mcloughlin, M., Ingram, S., Rendell, L. E., Lamoni, L. U., Kirke, A., Garland, E. C., ... Miranda, E. (2016). Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition. In R. Großmann, & G. Hajdu (Eds.), Proceedings SMC 2016 (pp. 274-280). (Proceedings of the SMC Conferences). Hochschule für Musik und Theater Hamburg.

Vancouver

Mcloughlin M, Ingram S, Rendell LE, Lamoni LU, Kirke A, Garland EC et al. Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition. In Großmann R, Hajdu G, editors, Proceedings SMC 2016. Hochschule für Musik und Theater Hamburg. 2016. p. 274-280. (Proceedings of the SMC Conferences).

Author

Mcloughlin, Michael ; Ingram, Simon ; Rendell, Luke Edward ; Lamoni, Luca Ubaldo ; Kirke, Alexis ; Garland, Ellen Clare ; Noad, Michael ; Miranda, Eduardo. / Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition. Proceedings SMC 2016. editor / Rolf Großmann ; Georg Hajdu. Hochschule für Musik und Theater Hamburg, 2016. pp. 274-280 (Proceedings of the SMC Conferences).

Bibtex - Download

@inproceedings{66f77a0469204c73b4651140aa421e93,
title = "Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition",
abstract = "Humpback whales (Megaptera Novaengliae) present one of the most complex displays of cultural transmission amongst non-humans. During breeding seasons, male humpback whales create long, hierarchical songs, which are shared amongst a population. Every male in the population conforms to the same song in a population. During the breeding season these songs slowly change and the song at the end of the breeding season is significantly different from the song heard at the start of the breeding season. The song of a population can also be replaced, if a new song from a different population is introduced.This is known as song revolution. Our research focuses on building computational multi agent models, which seek to recreate these phenomena observed in the wild.Our research relies on methods inspired by computational multi agent models for the evolution of music. This interdisciplinary approach has allowed us to adapt our model so that it may be used not only as a scientific tool, but also a creative tool for algorithmic composition. This paper discusses the model in detail, and then demonstrates how it may be adapted for use as an algorithmic composition tool.",
author = "Michael Mcloughlin and Simon Ingram and Rendell, {Luke Edward} and Lamoni, {Luca Ubaldo} and Alexis Kirke and Garland, {Ellen Clare} and Michael Noad and Eduardo Miranda",
year = "2016",
month = "8",
day = "31",
language = "English",
isbn = "9783000537004",
series = "Proceedings of the SMC Conferences",
publisher = "Hochschule f{\"u}r Musik und Theater Hamburg",
pages = "274--280",
editor = "Rolf Gro{\ss}mann and Georg Hajdu",
booktitle = "Proceedings SMC 2016",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Adapting a computational multi agent model for humpback whale song research for use as a tool for algorithmic composition

AU - Mcloughlin, Michael

AU - Ingram, Simon

AU - Rendell, Luke Edward

AU - Lamoni, Luca Ubaldo

AU - Kirke, Alexis

AU - Garland, Ellen Clare

AU - Noad, Michael

AU - Miranda, Eduardo

PY - 2016/8/31

Y1 - 2016/8/31

N2 - Humpback whales (Megaptera Novaengliae) present one of the most complex displays of cultural transmission amongst non-humans. During breeding seasons, male humpback whales create long, hierarchical songs, which are shared amongst a population. Every male in the population conforms to the same song in a population. During the breeding season these songs slowly change and the song at the end of the breeding season is significantly different from the song heard at the start of the breeding season. The song of a population can also be replaced, if a new song from a different population is introduced.This is known as song revolution. Our research focuses on building computational multi agent models, which seek to recreate these phenomena observed in the wild.Our research relies on methods inspired by computational multi agent models for the evolution of music. This interdisciplinary approach has allowed us to adapt our model so that it may be used not only as a scientific tool, but also a creative tool for algorithmic composition. This paper discusses the model in detail, and then demonstrates how it may be adapted for use as an algorithmic composition tool.

AB - Humpback whales (Megaptera Novaengliae) present one of the most complex displays of cultural transmission amongst non-humans. During breeding seasons, male humpback whales create long, hierarchical songs, which are shared amongst a population. Every male in the population conforms to the same song in a population. During the breeding season these songs slowly change and the song at the end of the breeding season is significantly different from the song heard at the start of the breeding season. The song of a population can also be replaced, if a new song from a different population is introduced.This is known as song revolution. Our research focuses on building computational multi agent models, which seek to recreate these phenomena observed in the wild.Our research relies on methods inspired by computational multi agent models for the evolution of music. This interdisciplinary approach has allowed us to adapt our model so that it may be used not only as a scientific tool, but also a creative tool for algorithmic composition. This paper discusses the model in detail, and then demonstrates how it may be adapted for use as an algorithmic composition tool.

UR - http://quintetnet.hfmt-hamburg.de/SMC2016/wp-content/uploads/2016/09/SMC2016_proceedings_final.pdf

M3 - Conference contribution

SN - 9783000537004

T3 - Proceedings of the SMC Conferences

SP - 274

EP - 280

BT - Proceedings SMC 2016

A2 - Großmann, Rolf

A2 - Hajdu, Georg

PB - Hochschule für Musik und Theater Hamburg

ER -

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ID: 247542422

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