A Music Similarity Model Based on Data Analysis and Algorithm Application

Jia, Yunsong; Liu, Yiming

doi:10.1109/aiid51893.2021.9456465

Cited by 2 publications

(1 citation statement)

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“…The applicable fields of music structural analysis (MSA) are musicology, music theory, and music cognition. Numerous methods [1][2][3][4][5] have been developed in the structural analysis of music based on similarities between a given musical data and other pieces of music. Some well-known similarity comparison methods are note-based (string matching approach), frame-based (geometric representation), ground truth-based, probability matching, and N-gram (based on pitch and rhythm dimensions) [6,7].…”

Section: Introductionmentioning

confidence: 99%

Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets

2022

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This paper presents two methods to understand the rhythmic patterns of the voice in Korean traditional music called Pansori. We used semantic segmentation and classification-based structural analysis methods to segment the seven rhythmic categories of Pansori. We propose two datasets; one is for rhythm classification and one is for segmentation. Two classification and two segmentation neural networks are trained and tested in an end-to-end manner. The standard HR network and DeepLabV3+ network are used for rhythm segmentation. A modified HR network and a novel GlocalMuseNet are used for the classification of music rhythm. The GlocalMuseNet outperforms the HR network for Pansori rhythm classification. A novel segmentation model (a modified HR network) is proposed for Pansori rhythm segmentation. The results show that the DeepLabV3+ network is superior to the HR network. The classifier networks are used for time-varying rhythm classification that behaves as the segmentation using overlapping window frames in a spectral representation of audio. Semantic segmentation using the DeepLabV3+ and the HR network shows better results than the classification-based structural analysis methods used in this work; however, the annotation process is relatively time-consuming and costly.

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