A new method for predominant melody extraction from polyphonic music signals based on harmonic structure is proposed. The proposed method first extracts a set of fundamental frequency candidates by analyzing the distance between spectral peaks. Then, the predominant fundamental frequency is selected by pitch tracking according to the harmonic strength of the selected candidates. Finally, the method runs pitch smoothing on a large temporal scale for eliminating pitch doubling error, and conducts voicing frame detection. The proposed method shows the best overall performance for ADC 2004 DB in the MIREX 2011 audio melody extraction task.

In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.