Folk music structural segment classification using GRU-based hierarchical attention network

This paper introduces a structural classification scheme for folk music using a multimodal fusion of musical features and textual information. A data corpus of Thiruvathirakali music (a popular folk dance in India) is compiled for this purpose. The musical features are learned using a gated recurrent unit (GRU) with an attention mechanism from musical texture features. A term frequency-inverse document frequency (TF-IDF) model is employed to derive text-based features from transcription data. Early integration of the GRU-derived features and TF-IDF features is employed for multimodal fusion. The performance is evaluated using Thiruvathirakali music corpus. The performance of the proposed system is also compared with that of a support vector machine-based classifier. The proposed system reports an overall precision, recall and F1 measure of 0.81 each for the GRU-attention-based model for multi-modal fusion. The scope of the proposed work for structural segmentation is also demonstrated with the help of an example.