Open AccessRaga classification based on pitch co-occurrence based features
Author(s) -
Vibhavari Rajadnya,
Kalyani R. Joshi
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i1.pp157-166
Subject(s) - pattern recognition (psychology) , computer science , classifier (uml) , speech recognition , artificial intelligence
Analysis and classification of raga is the need of time especially in music industry. With the presence of abundance of multimedia data on internet, it is imperative to develop appropriate tools to classify ragas. In this work, an attempt has been made to use occurrence pattern of pitch based svara (note) for classification. Sequence of notes is an important cue in the raga classification. Pitch based svara (note) profile is formed. This pattern presents in the signal along with its statistical distribution can be characterized using co-occurrence matrix. Proposed note co-occurrence matrix summarizes this aspect. This matrix captures both tonal and temporal aspects of melody. Ragas differ in terms of distribution of spectral power. K-nearest neighbor (KNN) has been used as the classifier. Publicly available database consisting of 300 recordings of 30 Hindustani ragas consisting of 130 hours of audio recordings stored as 160 kbps mp3 fileswhich is part of CompMusic project is used. Leave one out validation strategy is used to evaluate the performance. Experimental result indicates the effectiveness of the proposed scheme which is giving accuracy of 93.7%.