Browsing by Author "Heshi, R."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Rhythm and timbre analysis for carnatic music processing
    (2016) Heshi, R.; Suma, S.M.; Koolagudi, S.G.; Bhandari, S.; Rao, K.S.
    In this work, an effort has been made to analyze rhythm and timbre related features to identify raga and tala from a piece of Carnatic music. Raga and Tala classification is performed using both rhythm and timbre features. Rhythm patterns and rhythm histogram are used as rhythm features. Zero crossing rate (ZCR), centroid, spectral roll-off, flux, entropy are used as timbre features. Music clips contain both instrumental and vocals. To find similarity between the feature vectors T-Test is used as a similarity measure. Further, classification is done using Gaussian Mixture Models (GMM). The results shows that the rhythm patterns are able to distinguish different ragas and talas with an average accuracy of 89.98 and 86.67 % respectively. � Springer India 2016.
  • No Thumbnail Available
    Item
    Rhythm and timbre analysis for carnatic music processing
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2016) Heshi, R.; Suma, S.M.; Koolagudi, S.G.; Bhandari, S.; Sreenivasa Rao, K.S.
    In this work, an effort has been made to analyze rhythm and timbre related features to identify raga and tala from a piece of Carnatic music. Raga and Tala classification is performed using both rhythm and timbre features. Rhythm patterns and rhythm histogram are used as rhythm features. Zero crossing rate (ZCR), centroid, spectral roll-off, flux, entropy are used as timbre features. Music clips contain both instrumental and vocals. To find similarity between the feature vectors T-Test is used as a similarity measure. Further, classification is done using Gaussian Mixture Models (GMM). The results shows that the rhythm patterns are able to distinguish different ragas and talas with an average accuracy of 89.98 and 86.67 % respectively. © Springer India 2016.