Off-Grid Fundamental Frequency Estimation

Johan Sward, Hongbin Li, Andreas Jakobsson

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In this paper, we propose a gridless method for estimating an unknown number of fundamental frequencies. Starting with a conventional dictionary matrix, containing sets of candidate fundamental frequencies and their corresponding harmonics, a nonconvex log-sum cost function is formed such that it imposes the harmonic structure and treats every fundamental frequency in the dictionary as a parameter. The cost function is iteratively decreased by minimizing a surrogate function, and, in each iteration, the fundamental frequencies are refined, whereas redundant parameters are omitted from the dictionary. The proposed method is tested on both real and simulated data, showing its preferred performance as compared to other state-of-the-art multipitch estimators.

Original languageEnglish
Pages (from-to)296-303
Number of pages8
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume26
Issue number2
DOIs
StatePublished - Feb 2018

Keywords

  • Multi-Pitch estimation
  • Terms-Grid mismatch
  • iterative reweighted methods
  • super-resolution

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