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A harmonization model with partial fuzzy knowledge

机译:具有部分模糊知识的协调模型

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The paper presents a knowledge-driven harmonization model for tonal music partially using fuzzy knowledge. Automatic harmonization could be seen as a complicated classification problem: an algorithm processes a music notation document and generates class labels (harmonic functions). The proposed model could be seen as an Expert System, based largely on the music theory with partial fuzzy knowledge. It emphasizes universality - a possibility of obtaining varied but controllable harmonies. It is directly tunable by changing the internal parameters of harmonization mechanisms, as well as an importance weight corresponding to each mechanism. The proposed harmonization model allows also for relatively easy interpretation of internal knowledge.
机译:本文提出了一种部分利用模糊知识的知识驱动的音调和声模型。自动和声可以看作是一个复杂的分类问题:一种算法处理音乐符号文档并生成类标签(和声功能)。所提出的模型可以看作是专家系统,主要基于带有部分模糊知识的音乐理论。它强调普遍性-获得各种但可控的和谐的可能性。通过更改协调机制的内部参数以及与每个机制相对应的重要性权重,可以直接对其进行调整。提出的协调模型还允许相对容易地解释内部知识。

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