An overview of the CATE algorithms for real-time pitch determination

Bahja, Fadoua; Martino, Joseph Di; Elhaj, El Hassan Ibn; Aboutajdine, Driss

doi:10.1007/s11760-013-0488-4

Cited by 4 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… eCATE++ for enhanced Circular Autocorrelation of the Temporal Excitation (Bahja et al. 2013 ) is an algorithm for pitch detection based on an implicit circular autocorrelation of the glottal excitation signal. DWT and DT-CWT concern the two wavelet algorithms used in our approach under the voicing decision presented above.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…2012 ), YIN (De Cheveigné and Kawahara 2002 ), CEP (Noll 1967 ), PRAAT (Krusback and Niederjohn 1991 ) and eCATE++ (Bahja et al. 2013 ) pitch algorithms. In order to compare the performance of pitch determination algorithms, we propose to estimate the voicing decision results using the following error rates: The Gross Pitch Error (GPE) (Nakatani et al.…”

Section: Resultsmentioning

confidence: 99%

“…To make this comparison possible, we calculated the MFPE (Mean of Fine Pitch Error) which measures the bias of the F 0 estimation when no gross estimation error occurred (Bahja et al. 2013 ; Chu and Alwan 2012 ). where denotes the set of all the frames in which no gross error occurs and (for “Fine Error”) is equal to .…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

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

Bahja¹,

Martino²,

Elhaj³

et al. 2016

SpringerPlus

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%