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音乐教育中人工智能的运用 Music Education [16]

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:24821

论文字数:8929论文编号:org201407062205435872语种:英语 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。

s. Baker suggests the way a performer perceives the grouping structure of a piece has in influence on their performance.


Expert listeners may not agree with the structure used however, they do agree whether it is plausible or not. AI theories of group phrasing are imperfect. Thus AI systems sometimes produce groupings which a few expert listeners would find plausible.


This can be a problem for AI in education systems designed to help students in expressive performance. Baker devised a mechanism and representations so the system and student can interact and negotiate with each other. These can then be applied to teaching methods. The system and the tutor may negotiate learning styles, which strategies to use, what to do next etc. This system is said to support ‘learning by dialogue.’ Baker’s theory is primarily focused on teaching by negotiation rather than a practical system to use in a classroom.


8.1 Music composition: Supporting reflection

A similar outlook to Baker’s theory is Cook’s work (Cook, 1994; Cook and Morgan, 1995). It focuses on learning through dialogue. Cook’s work focuses on finding a framework which describes the internal dialogues for learner and teacher. The main purpose is to find a base for describing learning and teaching processes in music composition. There are two applications for this which include Interactive Learning Environment and a method for analysing protocols to understand teacher-student interaction.


Cook’s system COLERIDGE (Composition Learning Environment For Reflection about Intentions and Dialogue Goals in Education) mainly focuses on a higher level creative activities. His system is designed to gain skills that support reflection and problem-seeking. John Dewey, 1916) states that reflection is the ‘intentional endeavour to discover specific connections between something which we do and the consequences which result.’ Students are encouraged not to just seek out problems but to also find solutions and improve their abilities to find a solution.


Reflection and problem-seeking are highly relevant in terms of music education. For example, a good composer will not only reflect and question her composition but also try to re-think and re-design her composition in order to make it her own, instead of just merely applying compositional methods and techniques. From an AI in education point of view reflection and problem-seeking is a very interesting approach to an open-ended domain such as music.


8.2 Summary and Conclusions

In open-ended areas such as music there are no rules to adhere to - creativity is the key. However, there is a vast difference between beginners and experts. There are a variety of ways to apply AI in music education such as ear training, music composition, music theory and listening. This paper has mainly focused on composition and some attention performance and listening. As I am a musician myself and enjoy the ‘art’ of composition I have a particular interest in this field.


The effectiveness of the Logo approach has been questioned. Critics state that it requires high skilled and experienced individuals to make it work and it is unclear where to assign credit to – the system or the individual. Th论文英语论文网提供整理,提供论文代写英语论文代写代写论文代写英语论文代写留学生论文代写英文论文留学生论文代写相关核心关键词搜索。

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