The Simulated Emergence of Chord Function
The Simulated Emergence of Chord Function
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DOI：
10.1007/978-3-030-72914-1_18
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yui Uehara;S. Tojo
中科院分区：
文献类型：
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作者：
Yui Uehara;S. Tojo
在本文中，我们提出了一个自主的，无监督学习的和弦分类，神经隐马尔可夫模型（HMM）的基础上，并将其扩展到半马尔可夫模型（HSMM），以整合这些额外的上下文，如音高类直方图，节拍位置，和前面的和弦序列。我们在一个最小的预处理数据集上训练我们的模型，该数据集是主要/次要片段的混合，期望模型能够根据上下文学习和弦簇，而不需要分配音调。此后，我们评估他们的表现困惑，并表明，增加的上下文将大大提高效率。此外，我们表明，所提出的模型反映了大，小在其状态转换的背景下，即使在混合调性训练。
In this paper, we propose an autonomous, unsupervised learning of chord classification, based on the Neural Hidden Markov Model (HMM), and extend it to the Semi-Makov Model (HSMM) to integrate such additional contexts as the pitch-class histogram, the beat positions, and the preceding chord sequences. We train our model on a minimally pre-processed dataset in a mixture of major/minor pieces, expecting the models to learn the chord clusters in accordance with the contexts without assignment of tonality. Thereafter, we evaluate their performance by perplexity, and show that the added contexts would considerably improve the efficiency. In addition, we show that the proposed model reflects the context of major and minor in its state transitions, even though trained in mixed tonality.