Automatic segmentation, learning and retrieval of melodies using a self-organizing neural network

We introduce a neural network, known as SONNETMAP, capable of automatic segmentation, learning and retrieval of melodies. SONNET-MAP is a synthesis of the SONNET (Self-Organizing Neural NETwork) architecture (Nigrin, 1993) and an associative map derived from ARTMAP (Carpenter, Grossberg, and Reynolds, 1991). SONNET-MAP automatically segments a melody based on pitch and rhythmic grouping cues. Separate SONNET modules represent the pitch and rhythm dimensions of each segmented phrase independently, with two associative maps fusing these representations at the phrase level. Further SONNET modules aggregate these phrases forming a hierarchical memory structure that encompasses the entire melody. In addition, melodic queries may be used to retrieve any encoded melody. As far as we are aware, SONNET-MAP is the first self-organizing neural network architecture capable of automatically segmenting and retrieving melodies based on both pitch and rhythm.