Paper
1 October 2011 Comparison study between dyadic wavelet transform and modified higher order moment
Jeiran Choupan, Seyed Ghorshi, Mohammad Mortazavi, Farshid Sepehrband
Author Affiliations +
Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 82855F (2011) https://doi.org/10.1117/12.913400
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt
Abstract
Pitch detection is the process of determining the period of the vocal cords closure or in another word the time duration of one glottal closed, open and returning phase. Dyadic wavelets transform (DyWT) and modified higher order moment, are two pitch detection methods. DyWT is an accurate pitch detection method, however it has less accuracy compared to modified higher order moment. On the other hand modified higher order moment has high computational complexity and is time consuming. The DyWT pitch period detection is based on a two pass dyadic wavelet transform over a signal. Modified higher order moment is based on autocorrelation function (ACF) and in this method the speech signal has been split into a positive-amplitude and a negative-amplitude. For window length selection a larger window length that consists of current and several past frames followed by a smaller window length is used. In this paper we try to compare these two pitch detection methods in terms of accuracy, computational complexity and robustness to noise.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeiran Choupan, Seyed Ghorshi, Mohammad Mortazavi, and Farshid Sepehrband "Comparison study between dyadic wavelet transform and modified higher order moment", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82855F (1 October 2011); https://doi.org/10.1117/12.913400
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Wavelets

Signal detection

Wavelet transforms

Signal to noise ratio

Continuous wavelet transforms

Detection and tracking algorithms

Convolution

Back to Top