In many non-Western musical traditions, such as North Indian classical music (NICM), melodies do not conform to the major and minor modes, and they commonly use tunings that have no fixed reference (e.g., A = 440 Hz). We present a novel method for joint tonic and raag recognition in NICM from audio, based on pitch distributions. We systematically compare the accuracy of several methods using these tonal features when combined with instance-based (nearest-neighbor) and Bayesian classifiers. We find ...
In many non-Western musical traditions, such as North Indian classical music (NICM), melodies do not conform to the major and minor modes, and they commonly use tunings that have no fixed reference (e.g., A = 440 Hz). We present a novel method for joint tonic and raag recognition in NICM from audio, based on pitch distributions. We systematically compare the accuracy of several methods using these tonal features when combined with instance-based (nearest-neighbor) and Bayesian classifiers. We find that, when compared with a standard twelve-dimensional pitch class distribution that estimates the relative frequency of each of the chromatic pitches, smoother and more continuous tonal representations offer significant performance advantages, particularly when combined with appropriate classification techniques. Best results are obtained using a kernel-density pitch distribution along with a nearest-neighbor classifier using Bhattacharyya distance, attaining a tonic error rate of 4.2 percent and raag error rate of 10.3 percent (with 21 different raag categories). These experiments suggest that tonal features based on pitch distributions are robust, reliable features that can be applied to complex melodic music.
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