Recursive Estimation Method for Bilinear Systems by Using the Hierarchical Identification Principle

2019 ◽  
pp. 979-987
Author(s):  
Ling Xu ◽  
Xiao Zhang ◽  
Feng Ding
Keyword(s):  
Estimation Method ◽  
Bilinear Systems ◽  
Recursive Estimation ◽  
Hierarchical Identification

scholarly journals Estimation and tracking of fundamental, 2nd and 3d harmonic frequencies for spectrogram normalization in speech recognition

2012 ◽  
Vol 60 (1) ◽  
pp. 71-81
Author(s):  
K. Fujimoto ◽  
N. Hamada ◽  
W. Kasprzak
Keyword(s):  
Speech Recognition ◽  
Fundamental Frequency ◽  
Likelihood Function ◽  
Estimation Method ◽  
Recursive Estimation ◽  
Accurate Estimation ◽  
Harmonic Frequencies ◽  
Music Signal ◽  
Speech Features ◽  
Normalization Scheme

Estimation and tracking of fundamental, 2nd and 3d harmonic frequencies for spectrogram normalization in speech recognitionA stable and accurate estimation of the fundamental frequency (pitch,F0) is an important requirement in speech and music signal analysis, in tasks like automatic speech recognition and extraction of target signal in noisy environment. In this paper, we propose a pitch-related spectrogram normalization scheme to improve the speaker - independency of standard speech features. A very accurate estimation of the fundamental frequency is a must. Hence, we develop a non-parametric recursive estimation method ofF0 and its 2nd and 3d harmonic frequencies in noisy circumstances. The proposed method is different from typical Kalman and particle filter methods in the way that no particular sum of sinusoidal model is used. Also we tend to estimate F0 and its lower harmonics by using novel likelihood function. Through experiments under various noise levels, the proposed method is proved to be more accurate than other conventional methods. The spectrogram normalization scheme makes a mapping of real harmonic structure to a normalized structure. Results obtained for voiced phonemes show an increase in stability of the standard speech features - the average within-phoneme distance of the MFCC features for voiced phonemes can be decreased by several percent.

scholarly journals Closed-Loop Estimation for Randomly Sampled Measurements in Target Tracking System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jin Xue-bo ◽  
Lian Xiao-feng ◽  
Su Ting-li ◽  
Shi Yan ◽  
Miao Bei-bei
Keyword(s):  
Target Tracking ◽  
Dynamic Model ◽  
Closed Loop ◽  
Tracking System ◽  
Estimation Method ◽  
Dynamic Parameter ◽  
Sampling Interval ◽  
Recursive Estimation ◽  
Irregular Sampling ◽  
Sampled Measurements

Many tracking applications need to deal with the randomly sampled measurements, for which the traditional recursive estimation method may fail. Moreover, getting the accurate dynamic model of the target becomes more difficult. Therefore, it is necessary to update the dynamic model with the real-time information of the tracking system. This paper provides a solution for the target tracking system with randomly sampling measurement. Here, the irregular sampling interval is transformed to a time-varying parameter by calculating the matrix exponential, and the dynamic parameter is estimated by the online estimated state with Yule-Walker method, which is called the closed-loop estimation. The convergence condition of the closed-loop estimation is proved. Simulations and experiments show that the closed-loop estimation method can obtain good estimation performance, even with very high irregular rate of sampling interval, and the developed model has a strong advantage for the long trajectory tracking comparing the other models.

scholarly journals Return Predictability of Stock Price Index in Tehran Stock Exchange

2013 ◽  
Vol 9 ◽  
pp. 59-64
Author(s):  
Mohsen Mehrara
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
Asset Price ◽  
Estimation Method ◽  
Return Predictability ◽  
Recursive Estimation ◽  
Parameter Estimates ◽  
Daily Stock Returns

The question of whether asset price changes are predictable has long been the subject of many studies. Many studies, using historical returns based on random walk tests, have shown that stock return is not predictable. We study return predictability of the Tehran Exchange Price Index (TEPIX) based on monthly data from 2000 to 2011. For forecasting the return, we used a recursive estimation method in which the parameter estimates were updated recursively in light of new weekly observations, and also its regressors were changed recursively according to the Schwarz Bayesian Criterion. The results show that the daily stock returns are not predictable using publicly available information.

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