Harmonic Sinusoidal+Noise Modeling of Audio based on Multiple F0 Estimation

This paper deals with the detection and tracking of multiple harmonic series. We consider a bootstrap approach based on prior estimation of F0 candidates and subsequent iterative adjustment of a harmonic sieve with simultaneous refinement of the F0 and inharmonicity factor. Experiments show that this simple approach is an interesting alternative to popular strategies, where partials are detected without harmonic constraints, and harmonic series are resolved from mixed sets afterwards. The most important advantage is that common problems of tonal/noise energy confusion in case of unconstrained peak detection are avoided. Moreover, we employ a popular LP-based tracking method which is generalized to dealing with harmonically related groups of partials by using a vector inner product as the prediction error measure. Two alternative extensions of the harmonic model are also proposed in the paper that result in greater naturalness of the reconstructed audio: an individual frequency deviation component and a complex narrowband individual amplitude envelope.