2 research outputs found
A corroborative study on improving pitch determination by time–frequency cepstrum decomposition using wavelets
An overview of the CATE algorithms for real-time pitch determination
International audienceIn this paper, we present a recent algorithm for pitch detection based on an implicit circular autocorrelation of the glottal excitation signal. This algorithm operates in real time without the use of any post-processing technique. This article focuses on the correction of the pitch contours estimated and on the reduction in classification errors in speech signals using simple voicing decision techniques. To evaluate the performance of our algorithms, we used the Bagshaw and Keele databases. We show in this study that the sum of the percentage of the unvoiced errors and the percentage of the voiced errors, for the male Bagshaw corpus, reaches a very good score of 14.67. For the female corpus, our results are also competitive compared to other algorithms using the same database. Concerning the Keele database, we succeed to obtain very good gross pitch error, voicing decision error and F0 frame error rates, respectively, 0.44, 0.65 and 1.55 % in the whole corpus