Pitch Detection in Polyphonic Music using Instrument Tone Models

We propose a hidden Markov model (HMM) based system to detect the pitch of an instrument in polyphonic music using an instrument tone model. Our system calculates at every time frame the salience of a pitch hypothesis based on the magnitudes of harmonics associated with the hypothesis. A hypothesis selection method is introduced to choose pitch hypotheses with sufficiently high salience as pitch candidates. Then the system applies an instrument model to evaluate the likelihood of each candidate. The transition probability between successive pitch points is constructed using the prior knowledge of the musical key of the input. Finally an HMM integrates the instrument likelihood and the pitch transition probability. Quantitative evaluation shows the proposed system performs well for different instruments. We also compare a Gaussian mixture model and kernel density estimation for instrument modeling, and find that kernel density estimation gives better overall performance while the Gaussian mixture model is more robust.