A corroborative study on improving pitch determination by time–frequency cepstrum decomposition using wavelets

Abstract : A new wavelet-based method is presented in this work for estimating and tracking the pitch period. The main idea of the proposed new approach consists in extracting the cepstrum excitation signal and applying on it a wavelet transform whose resulting approximation coefficients are smoothed, for a better pitch determination. Although the principle of the algorithms proposed has already been considered previously, the novelty of our methods relies in the use of powerful wavelet transforms well adapted to pitch determination. The wavelet transforms considered in this article are the discrete wavelet transform and the dual tree complex wavelet transform. This article, by all the provided experimental results, corroborates the idea of decomposing the cepstrum excitation by using wavelet transforms for improving pitch detection. Another interesting point of this article relies in using a simple but efficient voicing decision (which actually improves a similar voicing criterion we proposed in a preceding published study) which on one hand respects the real-time process with low latency and on the other hand allows obtaining low classifications errors. The accuracy of the proposed pitch tracking algorithms has been evaluated using the international Bagshaw and the Keele databases which include male and female speakers. Our various experimental results demonstrate that the proposed methods provide important performance improvements when compared with previously published pitch determination algorithms.
Type de document :
Article dans une revue
SpringerPlus, SpringerOpen, 2016, 〈10.1186/s40064-016-2162-0〉
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Contributeur : Joseph Di Martino <>
Soumis le : lundi 9 mai 2016 - 09:11:32
Dernière modification le : mardi 24 avril 2018 - 13:54:23

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Fadoua Bahja, Joseph Di Martino, El Hassan Ibn Elhaj, Driss Aboutajdine. A corroborative study on improving pitch determination by time–frequency cepstrum decomposition using wavelets. SpringerPlus, SpringerOpen, 2016, 〈10.1186/s40064-016-2162-0〉. 〈hal-01312747〉



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