Browsing by Author "Thota, S."

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    Scale independent raga identification using chromagram patterns and swara based features
    (2013) Dighe, P.; Agrawal, P.; Karnick, H.; Thota, S.; Raj, B.
    In Indian classical music a raga describes the constituent structure of notes in a musical piece. In this work, we investigate the problem of scale independent automatic raga identification by achieving state-of-the-art results using GMM based Hidden Markov Models over a collection of features consisting of chromagram patterns, mel-cepstrum coefficients and timbre features. We also perform the above task using 1) discrete HMMs and 2) classification trees over swara based features created from chromagrams using the concept of vadi of a raga.On a dataset of 4 ragas- darbari, khamaj, malhar and sohini; we have achieved an average accuracy of ? 97%. This is a certain improvement over previous works because they use the knowledge of scale used in the raga performance. We believe that with a more careful selection of features and by fusing results from multiple classifiers we should be able to improve results further. � 2013 IEEE.
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    Scale independent raga identification using chromagram patterns and swara based features
    (2013) Dighe, P.; Agrawal, P.; Karnick, H.; Thota, S.; Raj, B.
    In Indian classical music a raga describes the constituent structure of notes in a musical piece. In this work, we investigate the problem of scale independent automatic raga identification by achieving state-of-the-art results using GMM based Hidden Markov Models over a collection of features consisting of chromagram patterns, mel-cepstrum coefficients and timbre features. We also perform the above task using 1) discrete HMMs and 2) classification trees over swara based features created from chromagrams using the concept of vadi of a raga.On a dataset of 4 ragas- darbari, khamaj, malhar and sohini; we have achieved an average accuracy of ∼ 97%. This is a certain improvement over previous works because they use the knowledge of scale used in the raga performance. We believe that with a more careful selection of features and by fusing results from multiple classifiers we should be able to improve results further. © 2013 IEEE.