Horizontal and Vertical Integration/Segregation in Auditory Streaming: A Voice Separation Algorithm for Symbolic Musical Data

Listeners are thought to be capable of perceiving multiple voices in music. Adopting a perceptual view of musical ‘voice’ that corresponds to the notion of auditory stream, a computational model is developed that splits a musical score (symbolic musical data) into different voices. A single ‘voice’ may consist of more than one synchronous notes that are perceived as belonging to the same auditory stream; in this sense, the proposed algorithm, may separate a given musical work into fewer voices than the maximum number of notes in the greatest chord (e.g. a piece consisting of four or more concurrent notes may be separated simply into melody and accompaniment). This is paramount, not only in the study of auditory streaming per se, but also for developing MIR systems that enable pattern recognition and extraction within musically pertinent ‘voices’ (e.g. melodic lines). The algorithm is tested qualitatively and quantitatively against a small dataset that acts as groundtruth.