Abstract
Signal detection in noisy and nonstationary environments is very challenging. In this Letter, we study why the two types of recurrence times [Phys. Rev. Lett. 83 (1999) 3178] may be very useful for detecting weak transitions in signal dynamics. We particularly emphasize that the recurrence times of the second type may be more powerful in detecting transitions with very low energy. These features are illustrated by studying a number of speech signals with fricatives and plosives. We have also shown that the recurrence times of the first type, nevertheless, has the distinguished feature of being more robust to the noise level and less sensitive to the parameter change of the algorithm. Since throughout our study, we have not explored any features unique to the speech signals, the results shown here may indicate that these tools may be useful in many different applications. © 2003 Elsevier B.V. All rights reserved.
Recommended Citation
J. B. Gao et al., "Detection Of Weak Transitions In Signal Dynamics Using Recurrence Time Statistics," Physics Letters Section A General Atomic and Solid State Physics, vol. 317, no. 1 thru 2, pp. 64 - 72, Elsevier, Oct 2003.
The definitive version is available at https://doi.org/10.1016/j.physleta.2003.08.018
Department(s)
Electrical and Computer Engineering
Publication Status
Full Text Access
International Standard Serial Number (ISSN)
0375-9601
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Publication Date
13 Oct 2003
