The method is evaluated in the context of melody extraction and obtains promising results, per- forming comparably to a state-of-the-art heuristic-based algorithm. The proposed approach has the advantage that new contour features can be easily incorporated into the model without the need to manually devise rules to address each feature individually. The algorithm exploits the learned model to compute a “melodiness” index for each pitch contour, which is then used to select the melody out of all pitch contours generated for an excerpt of polyphonic music. In our current work, we present a method for the statistical modelling of these features, and propose an algorithm for melody extraction based on the obtained model. In previous studies we presented a melody extraction algo- rithm in which contour features are used in a heuristic man- ner to filter out non-melodic contours. Within the context of melody extraction, pitch contours represent time and frequency continuous sequences of pitch candidates out of which the melody must be selected. In this paper we present a method for the statistical charac- terisation of melodic pitch contours, and apply it to auto- matic melody extraction from polyphonic music signals.
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