@conference{Williams2021,

title = {Neuro-curation: A case study on the use of sonic enhancement of virtual museum exhibits},

author = {Duncan Williams and Ian Daly and Inas Al-Taie and Paola Di Giuseppantonio Di Franco and Micheal Tymkiw},

year  = {2021},

date = {2021-08-09},

publisher = {Audio Mostly 2021},

keywords = {Affective computing, Emotion, Music generation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Williams2018,

title = {BCI for ensemble music making and performance: why and how (not how and why)},

author = {Duncan Williams and Ian Daly},

year  = {2018},

date = {2018-01-04},

booktitle = {Together in Music: Expression, Performance and Communication in Ensembles},

address = {National Centre for Early Music, York},

keywords = {BCMI, EEG, Emotion, Music generation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Daly2016aBCMIconf,

title = {An Affective Brain-Computer Music Interface},

author = {Ian Daly and Duncan Williams and Alexis Kirke and James Weaver and Asad Malik and Faustina Hwang and Maitreyee Wairagkar and Eduardo Miranda and Slawomir J. Nasuto},

year  = {2016},

date = {2016-06-01},

booktitle = {BCI meeting 2016},

keywords = {Affective computing, BCMI, Classification, EEG, Music generation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Daly2015ACII,

title = {Identifying music-induced emotions from EEG for use in brain-computer music interfacing},

author = {Ian Daly and Duncan Williams and Asad Malik and James Weaver and Faustina Hwang and Alexis Kirke and Eduardo Miranda, and Slawomir J. Nasuto},

url = {https://www.computer.org/csdl/proceedings/acii/2015/9953/00/07344685.pdf},

year  = {2015},

date = {2015-09-01},

booktitle = {Proceedings of the 4th workshop on affective brain-computer interfaces at the ACII 2015},

pages = {923-929},

abstract = {Brain-computer music interfaces (BCMI) provide a method to modulate an individuals affective state via the selection or generation of music according to their current affective state. Potential applications of such systems may include entertainment of therapeutic applications. We outline a proposed design for such a BCMI and seek a method for automatically differentiating different music induced affective states. Band-power features are explored for use in automatically identifying music-induced affective states. Additionally, a linear discriminant analysis classifier and a support vector machine are evaluated with respect to their ability to classify music induced affective states from the electroencephalogram recorded during a BCMI calibration task. Accuracies of up to 79.5% (p < 0.001) are achieved with the support vector machine.},

keywords = {Affective computing, BCMI, Classification, EEG, Music generation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Daly2015CEEC,

title = {Towards Human-Computer Music Interaction: Evaluation of an Affectively-Driven Music Generator Via Galvanic Skin Response Measures},

author = {Ian Daly and Asad Malik and James Weaver and Faustina Hwang and Slawomir J. Nasuto and Duncan Williams and Alexis Kirke and Eduardo Miranda},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/Identifying-music-induced-emotions-from-EEG-for-use-in-brain-computer-music-interfacing.pdf},

doi = {10.1109/CEEC.2015.7332705},

year  = {2015},

date = {2015-09-01},

booktitle = {Proceedings of the Seventh Computer Science and Electronic Engineering Conference 2015 (CEEC'15)},

pages = {87 - 92},

publisher = {IEEE},

abstract = {An affectively driven music generation system is described and evaluated. The system is developed for the intended eventual use in human-computer interaction systems such as brain-computer music interfaces. It is evaluated for its ability to induce changes in a listeners affective state. The affectively-driven algorithmic composition system was used to generate a stimulus set covering 9 discrete sectors of a 2-dimensional affective space by means of a 16 channel feedforward artificial neural network. This system was used to generate 90 short pieces of music with specific affective intentions, 10 stimuli for each of the 9 sectors in the affective space. These pieces were played to 20 healthy participants, and it was observed that the music generation system induced the intended affective states in the participants. This is further verified by inspecting the galvanic skin response recorded from participants.},

keywords = {BCMI, GSR, Music generation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Williams2015,

title = {Dynamic game soundtrack generation in response to a continuously varying emotional trajectory},

author = {Duncan Williams and Alexis Kirke and Joel Eaton and Eduardo Miranda and Ian Daly and James Weaver and Etienne Roesch and Faustina Hwang and Slawomir Nasuto},

url = {http://www.aes.org/e-lib/browse.cfm?elib=17593},

year  = {2015},

date = {2015-02-11},

booktitle = {Proceedings of the 56th International Conference: Audio for Games (February 2015)},

pages = {2-2},

abstract = {Dynamic soundtracking presents various practical and aesthetic challenges to composers working with games. This paper presents an implementation of a system addressing some of these challenges with an affectively-driven music generation algorithm based on a second order Markov-model. The system can respond in real-time to emotional trajectories derived from 2-dimensions of affect on the circumplex model (arousal and valence), which are mapped to five musical parameters. A transition matrix is employed to vary the generated output in continuous response to the affective state intended by the gameplay.},

keywords = {Affective composition, Markov model, Music generation},

pubstate = {published},

tppubtype = {conference}

}

@conference{Williams2014conf,

title = {Evaluating perceptual separation in a pilot system for affective composition},

author = {Duncan Williams and Alexis Kirke and Eduardo Miranda and Ian Daly and Etienne Roesch and James Weaver and Slawomir J. Nasuto},

url = {http://cmr.soc.plymouth.ac.uk/pubs/ICMC_2014_DW.pdf},

year  = {2014},

date = {2014-08-01},

booktitle = {Proceedings of the joint Sound and Music Computing Conference and International Computer Music Conference},

abstract = {Research evaluating perceptual responses to music has identified many structural features as correlates that might be incorporated in computer music systems for affectively charged algorithmic composition and/or expressive music performance. In order to investigate the possible integration of isolated musical features to such a system, a discrete feature known to correlate some with emotional responses – rhythmic density – was selected from a literature review and incorporated into a prototype system. This system produces variation in rhythm density via a transformative process. A stimulus set created using this system was then subjected to a perceptual evaluation.



Pairwise comparisons were used to scale differences between 48 stimuli. Listener responses were analysed with Multidimensional scaling (MDS). The 2-Dimensional solution was then rotated to place the stimuli with the largest range of variation across the horizontal plane. 



Stimuli with variation in rhythmic density were placed further from the source material than stimuli that were generated by random permutation. This, combined with the striking similarity between the MDS scaling and that of the 2-dimensional emotional model used by some affective algorithmic composition systems, suggests that

isolated musical feature manipulation can now be used to parametrically control affectively charged automated composition in a larger system.},

keywords = {Affective composition, Music generation},

pubstate = {published},

tppubtype = {conference}

}