Autocorrelation Domain Research Articles

Time-Frequency Signal Representations Using Interpolations in Joint-Variable Domains

Time-frequency (TF) representations are a powerful tool for analyzing Doppler and micro-Doppler signals. These signals are frequently encountered in various radar applications. Data interpolators play a unique role in TF signal representations under missing samples. When applied in the instantaneous autocorrelation domain over the time variable, the low-pass filter characteristic underlying linear interpolators lends itself to cross-terms reduction in the ambiguity domain. This is in contrast to interpolation performed over the lag variable or a direct interpolation of the raw data. We demonstrate the interpolator performance in both the time domain and the time-lag domain and compare it with sparse signal reconstruction, which exploits the local sparsity property assumed by most Doppler radar signals.

Research on Computer Information Processing Technology Based on Signal Autocorrelation Domain

The paper establishes signal correlation processing mathematical model according to Ordinary differential equations with boundary value, which is introduced into the analysis on basketball virtual simulation sliding technique, and it designs the signal autocorrelation computer processing system. In order to verify the reliability and practicality of the system in the basketball technology analysis, this paper extracts the temperature signal using the FLUENT software, it obtains the isotherm diagram and temperature distribution cloud picture, verifies the effectiveness of the system. Finally, it verifies the practicality system in basketball technology analysis, taking analysis of back gliding speed rhythm of basketball as an example, and it gets the basketball shuffle rhythm control 3D signal response curves, which provides technical reference for basketball players.

An Eigen Domain Interference Rejection Combining Algorithm for Narrowband Interference Suppression

In this letter, we propose an eigen domain interference rejection combining (E-IRC) algorithm to suppress the narrowband interference (NBI). The proposed E-IRC algorithm explores the difference between the time domain autocorrelation matrices of the NBI and the desired signal and implements the IRC in the eigen domain. Compared with the traditional IRC, which rejects the interference in the spatial domain, the proposed E-IRC algorithm exploits the characteristics of the received signal in both the time and spatial domain to suppress the NBI and thus yields better performance. Simulation results are presented to demonstrate the advantage of the proposed algorithm.

Study of Ranging System Using Pseudo-Random Continuous Wave Signal

Pseudo-random signal has potential value as ranging system’s signal since it has noise-like character, excellent autocorrelation and large time bandwidth character. The theory of the ranging system based on pseudo-random signal and how system works will be introduced in detail in this paper. We learned that the quality of signal generated by pseudo-random signal generator is very good through observing frequency domain chart, autocorrelation chart and time domain chart of signal. Quite accurate result can be got through dealing with experiment dates. It turned out that pseudo-random signal can be applied efficiently in ranging system and accuracy is quite high in field experiment. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4458 Full Text: PDF

Application of Correlation and Coherence Functions in Diagnostic Systems

Main problem in operation of diagnostic systems of power transmission systems is analysis of the energy of these harmonic components of the signal, which could be considered as diagnostic symptoms. Measured values can be affected i.a. by energy of random effects or other, less important components of the signal. Coherence and correlation functions are ones of classical methods of signal analysis being applied to analyze deterministic signals affected by random noise. In time domain autocorrelation function allows to determine time cohesion between adjacent fragments of analyzed process, shifted by different time values. In the paper author describes the method of minimization the impact of disturbances on signals being analyzed using basic properties of autocorrelation function. For the analysis of deterministic phenomena (including nonlinear) ordinary coherence function was applied. Showed examples were implemented and solved in MATLAB environment. The method was verified by analysis of model signals and signals (vibration accelerations) recorded on real object.

FPGA implementation and performance study of spectrum sensing based on entropy estimation using cyclic features

► A Spectrum sensing method based on entropy estimation using cyclostationary features. ► It outperforms energy, cyclostationary and entropy detection techniques. ► Proposed method detects signals up to −19 dB SNR with single node. ► It detects signals up to SNR −24 dB with five nodes in cooperation. ► The algorithm is implemented in Xilinx Virtex-4 Field Programmable Gate Array. This work presents a spectrum sensing technique based on the entropy of frequency domain autocorrelation of receiving signal at different cyclic frequencies. The performance of the proposed sensing technique is compared with other sensing techniques such as energy detection using Bayesian and Neyman–Pearson criteria, entropy estimation under frequency domain, cyclostationary feature detection. The performance of sensing algorithms is also analyzed for single node and multinode/cooperative environment under most probable channel effects such as fading, shadowing, receiver’s uncertainty and free space path loss using Monte-Carlo methods. Simulation results reveal that the proposed sensing technique is able to detect signals of signal-to-noise ratio up to −24 dB with five nodes in cooperation while maintaining a false alarm probability of 0.1 and a detection probability of 0.9. The proposed sensing algorithm is also implemented in Virtex-4 Field Programmable Gate Arrays.

Performance analysis of AND gates based on four-wave-mixing for application in optical-code division multiple access systems

In this paper, we study the performance of all optical AND gates based on the four-wave-mixing (FWM) process, for application in the receivers of the spectral phase encoded optical code division multiple access (SPE-OCDMA) systems. For the performance analysis, the spectral domain auto-correlation function of the multiple access interference signal at the output of the optical AND gate is derived and then mean and variance of the photo-detector current are calculated. For solving the Fourier transform of FWM non-linear Schrodinger equations, spectral domain Volterra series are used and the solution of these coupled equations is approximated with the first three terms of the Volterra expansion. Finally, the performance of AND gating receivers in SPE-OCDMA systems is analysed and the bit error rate in terms of the pump power and the number of active users is depicted. In addition, a compression between the performance of the time-gating receivers, base on FWM in synchronous mode and other non-linear receivers such as second harmonic generation and self-phase modulation is made.

An Autocorrelation-Based Interference Rejection Signal Detection Scheme

This presentation proposed an autocorrelation-based signal detection scheme to get a better resulting. The signal detection scheme is combined by Autocorrelation Filter and an improved linear Minimum Mean Square Error (MMSE) estimator. The Autocorrelation Filter is used to reject the interference, and the improved linear MMSE estimator is used to get the least error. Both two methods are based on the non-overlapping property of the signals in autocorrelation domain. The theory consequence and Simulation results indicate that this signal detection scheme can achieve a high detection quality.

Noise-Robust Pitch Detection using Auto-correlation Function with Enhancements

An efficient noise-robust pitch detection algorithm is proposed in this paper. The algorithm is based on time domain autocorrelation function (ACF). A bank of band-pass filters is used for competitive contribution of periodicity to select primary pitch candidates. A weighting criterion that involves both increase and decrease in merit is applied to the candidates by exploiting the presence or the absence of pitch harmonics. Finally, a simple enhancement is integrated to smooth the pitch contour. The proposed algorithm is evaluated on TIMIT database in different types and levels of noise in terms of pitch and voice activity detection. The experimental results show the superiority of the proposed method over well known other methods.

Robust speaker recognition based on filtering in autocorrelation domain and sub-band feature recombination

This paper presents a new method to improve features derived from filtering in autocorrelation domain, which are called relative autocorrelation sequence mel-frequency cepstral coefficients (RAS-MFCCs), one of the successful features in autocorrelation domain for noise-robust speaker recognition. The RAS-MFCCs are derived by applying temporal filtering to autocorrelation sequences under the assumption that corrupting noise is stationary. However, the use of only the filtered sequences could cause performance degradation due to the use of restricted information, and the assumption that noise is stationary might result in leaving non-stationary noise components in filtered autocorrelation sequences in real environments. To compensate for the restricted information, we propose a multi-streaming feature extraction that uses autocorrelation sequences as well as temporally filtered autocorrelation sequences for feature extraction. Furthermore, a hybrid feature representation, in which the multi-streaming feature extraction and the sub-band feature recombination are combined, is proposed to reduce the noise effects of autocorrelation sequences and the residual-noise effects of temporally filtered autocorrelation sequences. To evaluate the effectiveness of the proposed hybrid speaker recognition system in noisy conditions, we use the TIMIT database and the NTIMIT database. Experiments on the TIMIT database prove the effectiveness of the proposed hybrid method by reducing errors up to 26% and 14% over the conventional RAS-MFCCs in speaker identification and verification, respectively. On the NTIMIT database, the proposed hybrid feature representation provides error reduction of 24% and 18% over the conventional RAS-MFCCs for speaker identification and verification.

Role of Spectral Peaks in Autocoorelation Domain for Robust Speech Recognition

This paper presents a new front-end for robust speech recognition. This new front-end scenario focuses on the spectral features of the filtered speech signals in the autocorrelation domain. The autocorrelation domain is well known for its pole preserving and noise separation properties. In this paper we will use the autocorrelation domain as an appropriate candidate for robust feature extraction. The proposed method introduces a novel representation of speech for the cases where the speech signal is corrupted by additive noises. In this method, the speech features are computed by reducing additive noise effects via an initial filtering stage, followed by the extraction of autocorrelation spectrum peaks. Robust features based on theses peaks are derived by assuming that the corrupting noise is stationary in nature. A task of speaker-independent isolated-word recognition is used to demonstrate the efficiency of these robust features. The cases of white noise and colored noise such as factory, babble and F16 are tested. Experimental results show significant improvement in comparison to the results obtained using traditional front end methods. Further enhancement has been done by applying cepstral mean normalization (CMN) on the above extracted features.

Reduction of Average Path Length in Binary Decision Diagrams by Spectral Methods

This paper deals with analytic methods for the calculation and reduction of the average path lengths (APLs) in binary decision diagrams (BDDs). Usually, information-theoretic measures and information-theoretic techniques are used to construct BDDs of minimal APLs. Specifically, the mutual information between a Boolean function and its variables and the Shannon-Fano prefix coding are utilized. This paper deals with the problem of the APL reduction by using spectral techniques. Particularly, methods based on the properties of the Walsh spectrum of a Boolean function and its autocorrelation function are discussed. It is shown that the APL is a linear function of the autocorrelation values, that is, the APL depends on the Boolean function's properties in the time domain (autocorrelation) rather on the Boolean function's properties in the frequency domain (Walsh spectrum). In addition, it is shown that information-theoretic criteria like the mutual information or the conditional entropy are equivalent to frequency-domain criteria. Consequently, existing information-theoretic approaches for APL reduction that are based on mutual-information criterion or the Walsh transform coefficients may derive a suboptimal APL. The representation of the APL as a function of the autocorrelation values opens a way to determine the optimal ordering of the input variables analytically. Two procedures for APL reduction by ordering and by using linear combinations of the input variables are presented: (1) minimization by using the autocorrelation values and (2) minimization by using the mutual information between the Boolean function and a linear function of the input variables. The time-domain approach may derive a linearized BDD of a lower APL, whereas the information-theoretic approach has a lower computational complexity with comparable performance. Experimental results show the efficiency of the suggested techniques.

Fast algorithms for designing variable FIR notch filters

AbstractThe paper presents fast algorithms for designing finite impulse response (FIR) notch filters. The aim is to design a digital FIR notch filter so that the magnitude of the filter has a deep notch at a specified frequency, and as the notch frequency changes, the filter coefficients should be able to track the notch fast in real time. The filter design problem is first converted into a convex optimization problem in the autocorrelation domain. The frequency response of the autocorrelation of the filter impulse response is compared with the desired filter response and the integral square error is minimized with respect to the unknown autocorrelation coefficients. Spectral factorization is used to calculate the coefficients of the filter. In the optimization process, the computational advantage is obtained by exploiting the structure of the Hessian matrix which consists of a Toeplitz plus a Hankel matrix. Two methods have been used for solving the Toeplitz‐plus‐Hankel system of equations. In the first method, the computational time is reduced by using Block–Levinson's recursion for solving the Toeplitz‐plus‐Hankel system of matrices. In the second method, the conjugate gradient method with different preconditioners is used to solve the system. Comparative studies demonstrate the computational advantages of the latter. Both these algorithms have been used to obtain the autocorrelation coefficients of notch filters with different orders. The original filter coefficients are found by spectral factorization and each of these filters have been tested for filtering synthetic as well as real‐life signals. Copyright © 2007 John Wiley & Sons, Ltd.

Mel-Wiener Filter for Mel-LPC Based Speech Recognition

This paper proposes a Mel-Wiener filter to enhance Mel-LPC spectra in the presence of additive noise. The transfer function of the proposed filter is defined by using a first-order all-pass filter instead of unit delay. The filter coefficients are estimated based on minimization of the sum of the square error on the linear frequency scale without applying the bilinear transformation and efficiently implemented in the autocorrelation domain. The proposed filter does not require any time-frequency conversion, which saves a large amount of computational load. The performance of the proposed system is comparable to that of ETSI AFE. The optimum filter order is found to be 3, and thus filtering is computationally inexpensive. The computational cost of the proposed system except VAD is 53% of ETSI AFE.

Linear Prediction Using Refined Autocorrelation Function

This paper proposes a new technique for improving the performance of linear prediction analysis by utilizing a refined version of the autocorrelation function. Problems in analyzing voiced speech using linear prediction occur often due to the harmonic structure of the excitation source, which causes the autocorrelation function to be an aliased version of that of the vocal tract impulse response. To estimate the vocal tract characteristics accurately, however, the effect of aliasing must be eliminated. In this paper, we employ homomorphic deconvolution technique in the autocorrelation domain to eliminate the aliasing effect occurred due to periodicity. The resulted autocorrelation function of the vocal tract impulse response is found to produce significant improvement in estimating formant frequencies. The accuracy of formant estimation is verified on synthetic vowels for a wide range of pitch frequencies typical for male and female speakers. The validity of the proposed method is also illustrated by inspecting the spectral envelopes of natural speech spoken by high-pitched female speaker. The synthesis filter obtained by the current method is guaranteed to be stable, which makes the method superior to many of its alternatives.

Features based on filtering and spectral peaks in autocorrelation domain for robust speech recognition

In this paper, a set of features derived by filtering and spectral peak extraction in autocorrelation domain are proposed. We focus on the effect of the additive noise on speech recognition. Assuming that the channel characteristics and additive noises are stationary, these new features improve the robustness of speech recognition in noisy conditions. In this approach, initially, the autocorrelation sequence of a speech signal frame is computed. Filtering of the autocorrelation of speech signal is carried out in the second step, and then, the short-time power spectrum of speech is obtained from the speech signal through the fast Fourier transform. The power spectrum peaks are then calculated by differentiating the power spectrum with respect to frequency. The magnitudes of these peaks are then projected onto the mel-scale and pass the filter bank. Finally, a set of cepstral coefficients are derived from the outputs of the filter bank. The effectiveness of the new features for speech recognition in noisy conditions will be shown in this paper through a number of speech recognition experiments. A task of multi-speaker isolated-word recognition and another one of multi-speaker continuous speech recognition with various artificially added noises such as factory, babble, car and F16 were used in these experiments. Also, a set of experiments were carried out on Aurora 2 task. Experimental results show significant improvements under noisy conditions in comparison to the results obtained using traditional feature extraction methods. We have also reported the results obtained by applying cepstral mean normalization on the methods to get robust features against both additive noise and channel distortion.

Identification of AR parameters at a very low SNR using estimated spectral distribution in DCT domain

A simple and efficient method for system identification even at a very low signal-to-noise ratio (SNR) is presented. At an SNR as low as -7.5 dB, noise dominates the spectrum and system poles are almost lost in the profound noise. In the proposed method, an enhanced spectrum is estimated in the discrete cosine transform (DCT) domain using the least squares curve-fitting technique. The system modes that were previously indistinguishable become prominent in the enhanced spectrum. The system order is then overestimated using least squares higher order Yule-Walker (LSHOYW) equations to obtain better accuracy. The poles having higher strength in the autocorrelation domain are then identified as system poles.

Combination of autocorrelation-based features and projection measure technique for speaker identification

This paper presents a robust approach for speaker identification when the speech signal is corrupted by additive noise and channel distortion. Robust features are derived by assuming that the corrupting noise is stationary and the channel effect is fixed during an utterance. A two-step temporal filtering procedure on the autocorrelation sequence is proposed to minimize the effect of additive and convolutional noises. The first step applies a temporal filtering procedure in autocorrelation domain to remove the additive noise, and the second step is to perform the mean subtraction on the filtered autocorrelation sequence in logarithmic spectrum domain to remove the channel effect. No prior knowledge of noise characteristic is necessary. The additive noise can be a colored noise. Then the proposed robust feature is combined with the projection measure technique to gain further improvement in recognition accuracy. Experimental results show that the proposed method can significantly improve the performance of speaker identification task in noisy environment.

Anti-Jamming Filtering in the Autocorrelation Domain

An anti-jamming filtering technique is presented that it fully eliminates the jamming (or noncooperative) signal and its performance is noise-independent. This technique uses multiple receivers; a filtering process explores the nonoverlapping properties of the signals in the autocorrelation domain; and a matching of the input and the output statistics. A limited simulation study supports the theory.

Category invention in unsupervised learning.

This research aimed to discriminate between 2 general approaches to unsupervised category learning, one based on learning explicit correlational rules or associations within a stimulus domain (autocorrelation) and the other based on inventing separate categories to capture the correlational structure of the domain (category invention). An "attribute-listing" paradigm was used to index unsupervised learning in 3 experiments. Each experiment manipulated the order in which instances from 2 different categories were presented and evaluated the effects of this manipulation in terms of the 2 competing theoretical approaches to unsupervised learning. Strong evidence was found for the use by Ss of a discrete category invention process to learn the categories in these experiments. These results also suggest that attribute listing may be a valuable method for future investigations of unsupervised category learning.