Periodic signal modeling for the octave problem in music transcription

Precise automatic music transcription requires accurate modeling and identification of the spectral content of the audio signal. Whereas a deterministic model in terms of modulated periodic signals allows to distinguish different notes, the presence of multiple notes separated by octaves poses a big problem since they share the same periodicity, and hence completely overlapping spectral content. In this paper we propose the introduction of a spectral model to allow distinction of such mixtures of spectral content at various octaves. Cyclic correlations are estimated at its pitch and decomposed into even and odd parts, corresponding to even and odd harmonics.