We also explore the use of a convolutional neural network trained on mel-scale magnitude spectrograms for the boundary detection task to observe that while the implicit musical cues are largely learned by the network, it is less robust to deviations from training data characteristics. The dataset is augmented suitably for training and testing of the models to obtain new insights about the relevance of the different rhythmic, melodic and timbral cues in the automatic boundary detection task.

Both supervised and unsupervised approaches are tested using a dataset of commercial performance recordings that is manually annotated.

Motivated by the distinct musical properties of the sections and their corresponding acoustic correlates, we compute a number of features for the segment boundary detection task. In this work, we address the segmentation of a concert audio’s unmetered improvisatory section into musically meaningful segments at the highest time scale. 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.ĭhrupad vocal concerts exhibit a temporal evolution through a sequence of homogeneous sections marked by shared rhythmic characteristics. 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. The results show that the DeepLabV3+ network is superior to the HR network. A novel segmentation model (a modified HR network) is proposed for Pansori rhythm segmentation. The GlocalMuseNet outperforms the HR network for Pansori rhythm classification. A modified HR network and a novel GlocalMuseNet are used for the classification of music rhythm. The standard HR network and DeepLabV3+ network are used for rhythm segmentation. Two classification and two segmentation neural networks are trained and tested in an end-to-end manner. We propose two datasets one is for rhythm classification and one is for segmentation. We used semantic segmentation and classification-based structural analysis methods to segment the seven rhythmic categories of Pansori. This paper presents two methods to understand the rhythmic patterns of the voice in Korean traditional music called Pansori.

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