Abstract
An improved project based on decision trees in noisy environments is proposed for robust endpoints detection. Firstly, the noise level of the environment is estimated by wavelet decomposition, and then whether the denoising process is done according to the noise level is determined. Next, the thresholds are obtained by decision trees for the signal. Finally, endpoints are detected by the double thresholds on different importance of the energy and zero-crossing rate (ZCR) in the corresponding situation. The simulation results indicate that the proposed method based on noise estimation can obtain the same accurate data by computing less than the one with decision trees.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.