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音乐教育中人工智能的运用 Music Education

论文作者:www.51lunwen.org论文属性:硕士毕业论文 thesis登出时间:2014-07-07编辑:felicia点击率:7940

论文字数:8929论文编号:org201407062205435872语种:英语论文 English地区:中国价格:免费论文

关键词:principal approachesMusic EducationArtificial Intelligencecreativityproblem-seekingproblem-solving

摘要:音乐教育,是一个急需创造力、想象力、求知欲的教育领域,涉及到各个学科的知识内容,如音乐心理学、哲学、计算机科学等,随着音乐教育的不断发展,人工智能渐渐融入到音乐教育中。本文通过人工智能在音乐教育中的作用,探索其今后的发展。

摘要 Abstract

本文的目的是审查专注于人工智能的音乐教育方法。从学习者和教师双方角度来看,音乐是一个需要创造力、求知欲和有效解决问题的能力的领域,因此是在人工智能方面的一个具有挑战性的领域。智能辅导系统对于满足开放式思维的补救式教学是远远不够的。传统课堂方法有时是深受青睐的,因为导师可以关注个体差异,提高创造力和动力。

然而,人工智能是一种使没有传统音乐技能的人创造音乐的机制,一般来讲,应用于音乐创作的唯一的目标是写一些有趣的东西,本文将审查音乐教育方面不同的人工智能方法,这些方法被认为将会是:音乐智能辅导系统,基于人工智能的音乐工具;采用人工智能理论的高度交互界面,采用人工智能理论。


The goal of this paper is to review the principal approaches to Music Education with a focus on Artificial Intelligence (AI). Music is a domain which requires creativity, problem-seeking and problem-solving respectively, from both learner and teacher, therefore is a challenging domain in Artificial Intelligence. It is argued that remedial intelligent tutoring-systems are inadequate for teaching a subject that requires open-ended thinking. Traditional classroom methods are sometimes favoured because tutors can focus on individual differences and enhance creativity and motivation.


However, it can also be argued that AI is a mechanism which enables those without traditional musical skills to ‘create’ music. Almost the only goal that applies to music composition in general is ‘compose something interesting’ (Levitt, 1985). This paper will review different approaches to AI in Music Education. Approaches considered will be: Intelligent Tutoring Systems in Music; AI based Music Tools; highly interactive interfaces that employ AI theories.


1.前言 1. Introduction

本文将回顾音乐教育的一些人工智能的方法。这是一个特别的领域,涉及到跨学科的内容,包括教育界的特殊贡献,音乐,人工智能(AI)、音乐心理学、认知心理学、人机交互、哲学、计算机科学和其他一些学科内容。教育界的人工智能是一个非常广泛的领域,可以追溯到大约1970年,已经形成了自己的理论,方法和技术。为了方便,我们将遵循一个标准的约定,使用缩写词AI-ED指代人工智能教育。


This paper will review some of the approaches to using Artificial Intelligence in Music Education. This particular field is of high interdisciplinary and involves contributions from the fields of education, music, artificial intelligence (AI), the psychology of music, cognitive psychology, human computer interaction, Philosophy, computer science and many others. AI in education itself is a very broad field, dating from around 1970 (Carbonell, 1970) and has its own theories, methodologies and technologies. For brevity, we will abbreviate Artificial Intelligence in Education to AI-ED, following a standard convention.


Definitions

The scope of AI in Education (AI-ED) is not decisive, so it will be useful to consider some definitions. A common definition is: any application of AI techniques or methodologies to educational systems. Other definitions which focus more narrowly are, for example: any computer-based learning system which has some degree of autonomous decision-making with respect to some aspect of its interaction with its users (Holland, 1995). This definition suggests the requirement that AI techniques reason with the user at the point of interaction.


This might be in relation to best teaching approach, the subject being taught or any misconceptions or gaps in the student’s knowledge. However, AI-ED in a wider context is sometimes defined as: ‘the use AI methodologies and AI ways of thinking applied to discovering insights and methods for use in education, whether AI programs are involved at the point of delivery or not’ (Naughton, 1986). In practice, these contrasting approaches form a continuum.

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