A computationally efficient refinement of the fundamental frequency estimate for the Adaptive Harmonic Model

The full-band Adaptive Harmonic Model (aHM) can be used by the Adaptive Iterative Refinement (AIR) algorithm to accurately model the perceived characteristics of a speech recording. However, the Least Squares (LS) solution used in the current aHM-AIR makes the f₀ refinement in AIR time consuming, limiting the use of this algorithm for large databases. In this paper, a Peak Picking (PP) approach is suggested as a substitution to the LS solution. In order to integrate the adaptivity scheme of aHM in the PP approach, an adaptive Discrete Fourier Transform (aDFT) is also suggested in this paper, whose frequency basis can fully follow the frequency variations of the f₀ curve. Evaluations have shown an average time reduction of 5.5 times compared to the LS solution approach, while the quality of the resynthesis is preserved compared to the original aHM-AIR.