By Topic

Time series modeling for acoustic signals by a generalized adaptive function and its application to actual random noise data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Y. Mitani ; Fukuyama University, Fukuyama, Japan ; M. Ohta

As is well-known, it is impossible to model the actual time series data of acoustic signals by a simple equation. From the engineering viewpoint, effective prediction algorithm of the above time series signal is very important in the regulation and control problems for acoustic noise. It is also useful in saving the frequency band width in the communication line for acoustic information. From the above practical points of view, this paper describes a new trial for predicting the fluctuations of time series data by use of adaptive functions. The present prediction method is established based on not only the information of linear correlation but also that of higher order nonlinear correlations in the actual time series data. The validity of the proposed method has been experimentally confirmed by applying it to actual random noise data.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '86.  (Volume:11 )

Date of Conference:

Apr 1986