NDLI: Evolving Musical Sequences with N-Gram Based Trainable Fitness Functions

Content Provider	IEEE Xplore Digital Library
Author	Man Yat Lo Lucas, S.M.
Copyright Year	2006
Description	Author affiliation: Essex Univ., Colchester (Lucas, S.M.)
Abstract	Conventionally, automatic music composition is done by evolving music sequences whose fitness is evaluated by a human listener. This interactive approach has led to interesting results but is very time consuming. Here we propose a system that is capable of automatically generating music using an evolutionary algorithm (EA), replacing the human evaluation process with a trainable music evaluation algorithm. This algorithm can be trained on existing music samples, such as Mozart compositions for example. This kind of system could provide a fast and cheap music composition tool. The current evaluation system is implemented with an N-Gram language model. This paper discusses the system in two parts. Firstly, it describes the performance of the proposed music evaluation algorithm. Secondly, it discusses the impacts of different sequence-oriented genetic operators in the evolutionary algorithm. Part one of the experimental results show that the N-Gram model is able to distinguish the composer of piano compositions by Mozart, Beethoven and Chopin with up to 81.9% accuracy. Part two of the results show that some of the sequence-oriented operators increased the fitness of the generated melodies, but some operators did not. The impacts of these operators are discussed in the experimental results section. Significantly, the results also show that better classification accuracy does not necessarily lead to better evolved music, suggesting that perceptual relevance is also an important factor.
Starting Page	601
Ending Page	608
File Size	325311
Page Count	8
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688365
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation Humans Multiple signal classification Stochastic processes Differential equations Genetics Bars Stochastic systems Natural language processing Training data
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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