# Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets
## 1 Introduction  
Deep learning research on music information retrieval has generally focused on content-based filtering (affective state, artist, genre, music similarity) rather than musical structure, although musical structure analysis has wide applications in music information retrieval (MIR). 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]. The development of a computational model to learn from music content for structure analysis is an important and useful tool today to efficiently store and retrieve the digital content. In musicology, the musical structure expresses the contiguous, non-overlapping musical segments that compose a given audio signal. MSA identify the similar musical pieces and label them at different time scales. The automatic segmentation of music is a key issue in music browsing, searching, summarization and recommendation [8]. Segmenting music involves identifying boundaries between homogenous regions. Music segmentation is possible based on vocals, instruments, rhythm, harmony, pitch, and interval. The segmentation result shows musical boundaries, musical form and semantic labels such as verse, chorus or bridge [9, 10, 11]. The segmentation algorithm detects the locations where significant changes of statistical properties [12] or semantic information [13] occur. A perfect understanding of musical boundaries is a hard task even for a human, as highlighted in a review paper [14]. The paper highlights the real-world applications, current trends and challenges, existing methods, evaluation, dataset, and future direction of research for music structure analysis. Homogeneity, novelty, repetition, and regularity are some segmentation principles described in the review paper; among them, our approach is homogenous musical piece detection using a deep neural network. Past methods show interesting results in MSA using a non-data-driven approach. [PERSON] [8] proposed an EM-based HMM method for chord-based segmentation. Similarly, [PERSON] [11] used tonality and the recurrent structure of music for automatic music segmentation. The data-driven approaches are now massively used for structure boundaries determination of music. A CNN-based method [15] used audio spectrograms with self-similarity lag matrices. The method proposed a complex CNN structure with multiple input features fusion. The results are interesting but have some limitations because of limited training data and simple network architecture. The past research only conducted a similarity-based comparison of two-piece of music using the information of pitch, rhythm, vocals, or instrument. We proposed a novel technique for music structure analysis using rhythmic information at pixel labels. At the time of writing, we could not find such research that has been used to segment music according to rhythmic features. At present, we have several efficient segmentation algorithms but still have data scarcity to train them from scratch. We proposed two datasets and methods for MSA that could attract new researchers in this direction in the future. In this paper, we studied a structural analysis of Korean traditional music called Passori based on musical rhythms. Musical rhythm is a sound pattern composed of silent and singing notes. There are more than six unique rhythmic patterns in Passori excluding rests (silences) between each rhythmic category and narration. Some non-data-driven methods for MSA research [1; 16] using rhythmic information of music have been used in Western music. We use the current mainstream classification and semantic segmentation for MSA using deep learning.