Several features were compared with regard to recognition performance in a musical instrument recognition system. Both mel-frequency and linear prediction cepstral and delta cepstral coefficients were calculated. Linear prediction analysis was carried out both on a uniform and a warped frequency scale, and reflection coefficients were also used as features. The performance of earlier described ...

This thesis is concerned with the extension of the theory and computational techniques of time-series linear prediction to two-dimensional (2-D) random processes. 2-D random processes are encountered in image processing, array processing, and generally wherever data is spatially dependent. The fundamental problem of linear prediction is to determine a causal and causally invertible (minimumphas...

Considered are general, pure linear prediction schemes, where the prediction of the current sample is based on IIR-filtered past samples. Properties of these schemes are discussed, in particular, the whitening property, the realisation of the synthesis filter and its stability. The realisation problem as occurring in warped linear prediction is solved. Furthermore, it is proved that, at least f...

This paper proposes a modified linear prediction method for speech analysis, using two system identification methods—the least-square method and the instrument variable method—for the estimation of the coefficients of an all-pole filter. Whereas the linear prediction method estimates the coefficients of all-pole filters from speech signals, which are observed output signals, the system identifi...

Alkaline phosphatase (EC 3.1.3.1) isoenzymes in serum may be determined by multicomponent analysis of the enzyme activities in the presence of multiple inhibitors. To determine inhibition coefficients of the isoenzymes, we used multiple linear regression analysis to compare alkaline phosphatase activities in the presence of known inhibitors with electrophoretically determined isoenzyme activiti...

In this paper, cepstral features derived from the Differentiated Relative Higher Order Autocorrelation Sequence Spectrum (DRHOASS) are proposed for improving the robustness of a speech recognizer in the presence of background noise. Proposed method is analyzed and compared in terms of the autocorrelation coefficients they employ with the traditional feature extraction methods based on Linear Pe...

in the present study, the energetic and economic modeling of lentil and chickpea production in esfahan province of iran was conducted using adaptive neuro-fuzzy inference system (anfis) and linear regression. data were taken by interviewing and visiting of 140 lentil farms and 110 chickpea farms during 2014-2015 production period. the results showed that the yield and total energy consumption w...

In this paper, a feature extraction method for robust speech recognition in noisy environments is proposed. The proposed method is motivated by a biologically inspired auditory model which simulates the outer/middle ear filtering by a low-pass filter and the spectral behaviour of the cochlea by the Gammachirp auditory filterbank (GcFB). The speech recognition performance of our method is tested...

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant “à trous” wavelet transform which has the advantage of being shift-invariant. The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its...

The topic of this paper is formant frequency prediction using multiple linear regression. This technique provides robust formant frequency estimates but with limited precision. We apply this approach to predict formant frequencies of one male speaker comparing three different spectral representations. Mel scaled filterbanks are shown to perform slightly better than linear prediction based cepst...