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However, such textbook theories are too restrictive to represent diverse musical styles.
![transition for g major scale to b flat major transition for g major scale to b flat major](https://i.ytimg.com/vi/MlQOIXwvDNs/maxresdefault.jpg)
These regularities have been studied as harmony theory, which is employed in artificial intelligence for music such as structural analysis, recommendation, and music generation. In music, combinations of tones form chords, and they progress in a somewhat regular way. We further show that the transitions between chord categories reflect the difference of tonalities with a tendency consistent with known chord functions. We observe the chord categories effectively cover chords that appeared in the corpus. To this purpose, we pre-process the dataset minimally that is, we only transpose pieces so as not to possess key signatures and ignore octave positions in pitch events. With the help of these neural networks, the proposed model automatically learns hidden states that appropriately represent chord categories. Experimental results show that the added contexts considerably improve the perplexity. In this research, we employ hidden semi-Markov model to incorporate music metrical structure, and in addition, we combine the model with neural network components to embed context information such as beat positions and preceding chord sequences.
![transition for g major scale to b flat major transition for g major scale to b flat major](https://www.schoolofcomposition.com/wp-content/uploads/2019/01/8-G-major-scale.png)
This paper proposes an unsupervised learning of chord classification aiming at an autonomous recognition of chord functions.
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