Instrument identification and pitch estimation in multi-timbre polyphonic musical signals based on probabilistic mixture model decomposition

In this paper, we propose a method based on probabilistic mixture model decomposition that can simultaneously identify musical instrument types, estimate pitches and assign each pitch to its source instrument in monaural polyphonic audio containing multiple sources. In the proposed system, the probability density function (PDF) of the observed mixture note is treated as a weighted sum approximation of all possible note models. These note models, covering 14 instruments and all their possible pitches, describe their dynamic frequency envelopes in terms of probability. The weight coefficients, indicating the probabilities of the existence of pitches of a certain type of instrument, are estimated using the Expectation-Maximization (EM) algorithm. The weight coefficients are used to detect the types of source instruments and the pitches. The results of experiments involving 14 instruments within a designated pitch range F3–F6 (37 pitches) demonstrate a good discrimination capability, especially in instrument identification and instrument-pitch identification. For the entire system including the note onset detection tool, using quartet polyphonic recordings, the average F-measure values of instrument-pitch identification, instrument identification and pitch estimation were 55.4, 62.5 and 86 % respectively.