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We describe the results of the first large-scale raag recognition experiment. Raags are the central structure of In-dian classical music, each consisting of a unique set of complex melodic gestures. We construct a system to recognize raags based on pitch-class distributions (PCDs) and pitch-class dyad distributions (PCDDs) calculated directly from the audio(More)
Music is a cultural universal and a rich part of the human experience. However, little is known about common brain systems that support the processing and integration of extended, naturalistic 'real-world' music stimuli. We examined this question by presenting extended excerpts of symphonic music, and two pseudomusical stimuli in which the temporal and(More)
We present an audio chord recognition system based on a generalization of the Hidden Markov Model (HMM) in which the duration of chords is explicitly considered-a type of HMM referred to as a hidden semi-Markov model, or duration-explicit HMM (DHMM). We find that such a system recognizes chords at a level consistent with the state-of-the-art systems –(More)
In this paper, we present a set of simple and efficient regu-larized logistic regression algorithms to predict tags of music. We first vector-quantize the delta MFCC features using k-means and construct " bag-of-words " representation for each song. We then learn the parameters of these logistic regression algorithms from the " bag-of-words " vectors and(More)
Much of our enjoyment of music comes from its balance of predictability and surprise. Musical pitch fluctuations follow a 1/f power law that precisely achieves this balance. Musical rhythms, especially those of Western classical music, are considered highly regular and predictable, and this predictability has been hypothesized to underlie rhythm's(More)
A system that segments and labels tabla strokes from real performances is described. Performance is evaluated on a large database taken from three performers under different recording conditions, containing a total of 16,834 strokes. The current work extends previous work by Gillet and Richard (2003) on categorizing tabla strokes, by using a larger, more(More)
A system was constructed to automatically identify raags using pitch-class (PCDs) and pitch-class dyad distributions (PCDDs) derived from pitch-tracked performances. Classification performance was 94% in a 10-fold cross-validation test with 17 target raags. Using PCDs alone, performance was 75%, and 82% using only PCDDs. Best performance was attained using(More)