Polyphonic transcription based on temporal evolution of spectral similarity of gaussian mixture models

This paper describes a system to transcribe multitimbral polyphonic music based on a joint multiple-F0 estimation. In a frame level, all possible fundamental frequency (F0) candidates are selected. Using a competitive strategy, a spectral envelope is estimated for each combination composed of F0 candidates under assumption that a polyphonic sound can be modeled by a sum of weighted gaussian mixture models (GMM). Since in polyphonic music the current spectral content depends to a large extent of the immediately previous one, the winner combination is determined taking into account the highest spectral similarity regarding to the past music events which has been selected from a set of combinations that minimize the current spectral distance between input-GMM spectrums. Our system was tested using several pieces of real-world music recordings from RWC Music Database. Evaluation shows encouraging results compared to a recent state-of-the-art method.