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Differential Music : = Automated Music Generation Using LSTM Networks with Representation Based on Melodic and Harmonic Intervals.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Differential Music :/
其他題名:	Automated Music Generation Using LSTM Networks with Representation Based on Melodic and Harmonic Intervals.
作者:	Rafraf, Hooman.
面頁冊數:	1 online resource (90 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Musical composition. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29066282click for full text (PQDT)
ISBN:	9798802704806

Differential Music : = Automated Music Generation Using LSTM Networks with Representation Based on Melodic and Harmonic Intervals.
Rafraf, Hooman.

Differential Music :Automated Music Generation Using LSTM Networks with Representation Based on Melodic and Harmonic Intervals. - 1 online resource (90 pages)

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--University of Florida, 2022.

Includes bibliographical references

To be able to teach music to an artificial-intelligence (AI) model, regardless of its functionality, the music has to be encoded in a way that is understandable for a computer program. Examples of such "representations" of music include MIDI, piano-roll, MP3, MusicXML, etc. In the AI field, the representations are normally in the form of matrices of numbers. This research introduces a generative AI model for automated music composition with LSTM networks that uses a novel representation of music that is based on melodic and harmonic intervals in music rather than absolute pitch. Melodies are encoded as a series of intervals rather than a series of pitches, and chords are encoded as the set of intervals that each chord note makes with the melody at each moment. We will first introduce some of the suitable AI models as well as the literature on the use of artificial intelligence and machine learning in musical creativity. We will then focus on LSTM-based models and how they have been used for automated music generation. The existing representations are introduced and discussed. We will then build a model using LSTM networks and use our novel representation to train the model to write music. The resulting compositions are included and discussed as well.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798802704806Subjects--Topical Terms:

3289630
Musical composition.
Subjects--Index Terms:

Automated music generationIndex Terms--Genre/Form:

542853
Electronic books.

Differential Music : = Automated Music Generation Using LSTM Networks with Representation Based on Melodic and Harmonic Intervals.
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