Abstract
We propose a new method of applying Generative Theory of Tonal Music directly to a spectrogram of music to produce a time-span segmentation as hierarchical clustering. We first consider a vertically long rectangle in a spectrogram (bin) as a pitch event and a spectrogram as a sequence of bins. The texture feature of a bin is extracted using a gray level co-occurrence matrix to generate a sequence of the texture features. The proximity and change of phrases are calculated by the distance between the adjacent bins by their texture features. The global structures such as parallelism and repetition are detected by a self-similarity matrix of a sequence of bins. We develop an algorithm which is given a sequence of the boundary strength between adjacent bins, iteratively merges adjacent bins in the bottom-up manner, and finally generates a dendrogram, which corresponds to a time-span segmentation. We conducted an experiment with inputting Mozart’s K.331 and K.550 and obtained promising results although the algorithm does not take into account almost any musical knowledge such as pitch and harmony.
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Notes
- 1.
Since the space is limited, for more detail, see literatures [8, 5, 6].
- 2.
Note that \(b_{i,i+1}\) means the strength of boundary between bins \(b_i\) and \(b_{i+1}\), and \(b_{i,i+1 i+2}\) means that between \(b_i\) and \(b_{i+1 i+2}\).
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Acknowledgement
This work has been supported by JSPS Kakenhi 16H01744.
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Sawada, S., Takegawa, Y., Hirata, K. (2018). On Hierarchical Clustering of Spectrogram. In: Aramaki, M., Davies , M., Kronland-Martinet, R., Ystad, S. (eds) Music Technology with Swing. CMMR 2017. Lecture Notes in Computer Science(), vol 11265. Springer, Cham. https://doi.org/10.1007/978-3-030-01692-0_16
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