Spectral Refinement and its Application to Fundamental Frequency Estimation
Mohamed Krini, Gerhard Uwe Schmidt · 2007
In this paper a method for spectral refinement (SR) of speech and audio signals and its application to fundamental frequency estimation is presented. The SR procedure is applied as a post-processor on the output of a standard short-term frequency analysis. The algorithm is based on a linear combination of weighted subband signal vectors and thus has low computational complexity. The new scheme can be applied either as a refinement of only a subset of the frequency bands or as a refinement of the entire frequency range including the computation of additional frequency supporting points. Several algorithmic parts, e.g., noise suppression or fundamental frequency estimation, can achieve better results if a better resolution - at least in the lower frequency range - can be provided. In this contribution an enhanced fundamental frequency estimation method is proposed, that allows reliable operation at low signal-to-noise scenarios even for very low fundamental frequencies. Evaluations have shown that a significant improvement can be accomplished when utilizing the SR method as a pre-processor for fundamental frequency estimation.