Name
Abstract | ||
|---|---|---|
An algorithm based on dyadic wavelet transform (DyWT) has been developed for detecting pitch period. Pitch period is regarded as an important parameter in designing and developing automatic speaker recognition/identification systems. In this paper, we have developed two methods for detecting pitch period of synthetic signals. In the first method, we estimated the pitch period using the original signal by calculating reasonably accurate values for the dilation parameter L. Whereas, in the-second method, pitch period was estimated from the power spectrum of the signal. Several experiments were performed, under noisy and ideal environmental conditions, to evaluate the accuracy and robustness of the proposed methodology. It was observed from the experiments that the proposed techniques were successful in estimating pitch periods. The best case accuracy in the estimation was found to be approximately 100%. (C) 1999 Published by Elsevier Science Inc. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1016/S0020-0255(99)00055-9 | Inf. Sci. |
Keywords | Field | DocType |
speech signal,new dyadic wavelet algorithm,pitch period,wavelet transform,power spectrum,speech processing | Speech processing,Dilation (morphology),Audio time-scale/pitch modification,Pattern recognition,Pitch period,Robustness (computer science),Spectral density,Artificial intelligence,Pitch detection algorithm,Mathematics,Wavelet transform | Journal |
Volume | Issue | ISSN |
119 | 1-2 | 0020-0255 |
Citations | PageRank | References |
6 | 0.88 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad S. Obaidat | 1 | 2190 | 315.70 |
| C. Lee | 2 | 6 | 0.88 |
| B. Sadoun | 3 | 138 | 12.08 |
| David L. Nelson | 4 | 16 | 9.89 |