Default Bayesian Estimation of the Fundamental Frequency

Jesper Kjær Nielsen, Mads Græsbøll Christensen, Søren Holdt Jensen

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26 Citations (Scopus)
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Joint fundamental frequency and model order esti- mation is an important problem in several applications. In this paper, a default estimation algorithm based on a minimum of prior information is presented. The algorithm is developed in a Bayesian framework, and it can be applied to both real- and complex-valued discrete-time signals which may have missing samples or may have been sampled at a non-uniform sampling frequency. The observation model and prior distributions corre- sponding to the prior information are derived in a consistent fashion using maximum entropy and invariance arguments. Moreover, several approximations of the posterior distributions on the fundamental frequency and the model order are derived, and one of the state-of-the-art joint fundamental frequency and model order estimators is demonstrated to be a special case of one of these approximations. The performance of the approximations are evaluated in a small-scale simulation study on both synthetic and real world signals. The simulations indicate that the proposed algorithm yields more accurate results than previous algorithms. The simulation code is available online.
Original languageEnglish
JournalI E E E Transactions on Audio, Speech and Language Processing
Issue number3
Pages (from-to)598-610
Number of pages13
Publication statusPublished - Mar 2013


  • Fundamental frequency estimation
  • Bayesian model comparison
  • Zellner's g-prior


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