MusiCAN: Music Generation using Creative Adversarial Networks

Perera, Hiruni

dc.contributor.author	Perera, Hiruni
dc.date.accessioned	2024-03-01T06:43:57Z
dc.date.available	2024-03-01T06:43:57Z
dc.date.issued	2023
dc.identifier.citation	Perera, Hiruni (2023) MusiCAN: Music Generation using Creative Adversarial Networks. BSc. Dissertation, Informatics Institute of Technology	en_US
dc.identifier.issn	20191278
dc.identifier.uri	http://dlib.iit.ac.lk/xmlui/handle/123456789/1798
dc.description.abstract	"Computer creativity has been a topic of interest where discussions of advancements of AI have been concerned. This topic is further explored in through the aspect of music generation in this text. When speaking of music generation through AI the most effective i.e., the approach that yields the most promising results in terms of creativity would be unsupervised approaches. Spearheading the departments of music generation would be GANs. Creative Adversarial Networks (CANs) have been based off GANs and have shown promising results in the field of art. The author therefore will put CANs to the test on how well music generation can be attempted through the application of CANs in MusiCAN."	en_US
dc.language.iso	en	en_US
dc.publisher	IIT	en_US
dc.subject	Music generation	en_US
dc.subject	Generative Adversarial Networks	en_US
dc.subject	Creative Adversarial Networks	en_US
dc.title	MusiCAN: Music Generation using Creative Adversarial Networks	en_US
dc.type	Thesis	en_US