Linear predictive coding without Yule-Walker approximation for transient signal analysis: Application to switching noise

Improving the spatial resolution in CZT detectors using charge sharing effect and transient signal analysis: Simulation study

We are developing a method of time-frequency analysis on the basis of linear predictive coding (LPC). In contrast to standard LPC, which assumes an infinite number of samples, our method obtains instantaneous frequencies, instantaneous amplitude vary rates, and instantaneous amplitudes from a small number of samples. The time width for single local analysis can be much narrower than the period of oscillation of a given time series, enabling high resolution time-frequency analysis. We outline our method and use it to analyze electro-magnetic noise in comparison with short time Fourier transform to show its resolution.

This paper presents a new method for transient signal analysis in the frequency domain based on the continuous wavelet transform (CWT). The study involved testing signals obtained from an electronic switch that considers opening and closing operations. The source is connected to inductive, resistive, and capacitive loads. The resonance behaviors were analyzed, obtaining the result that the signal processing performed by CWT is better for spectrum analysis than the Fourier Transform (FT). The results show that the CWT achieves greater reliability, repeatability, high noise attenuation, and softness of the analyzed spectrum. This study shows the potential of WT for analyzing transient signals, specifically in the connection and disconnection of loads.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Results of an experimental study and the optimization of features for a conventional vector-quantization codebook-based automatic speaker identification (ASI) system are presented. Standard LPC (linear predictive coding) and a perceptually weighted feature termed PLP (perceptually based linear prediction) are compared using appropriate distance measures, namely, the log-likelihood, and three cepstral variants: constant weighting, the robot-power-sum, and the inverse variance. PLP features combined with a weighted cepstral measure are found to be consistently the best in a number of different digit-independent ASI experiments. Results support the hypothesis that the higher orders of PLP (>5) contain significant speaker-specific information, with ASI performance improving rapidly up to order 8, and then far more slowly yet consistently up to order 16. A similar pattern is seen for codebook size, with fast improvements up to size 64, with more gradual gains thereafter. >

Time–frequency analysis (TFA) methods can characterize the instantaneous frequency (IF) features of nonstationary signals by constructing the time-domain signal model. However, the traditional TFA methods are ineffective for transient signals that are discontinuous in the time domain and characterized by sharp frequency variation. Therefore, this article proposes a new TFA method called generalized transient-squeezing transform (GTST) to process transient signals by constructing a signal model in the frequency domain. First, the generalized group delay (GD) estimation is calculated based on the frequency-domain signal model. Then, the generalized transient-squeezing operator is constructed by iterative operations. Finally, the time–frequency (TF) energy is rearranged according to the constructed operator. The GTST provides a more compact TF representation (TFR) of the transient signal while maintaining reversibility. Numerical validation shows that the proposed method has higher TF concentration and accuracy in analyzing transient signals compared with other advanced TFA methods. Experimental validation shows the effectiveness of the proposed method in practical transient signal analysis.

Linear predictive coding (LPC) parameters are widely used in various speech coding applications for representing the short-time spectral envelope information of speech [1]. For low bit rate speech coding applications, it is important to quantize these parameters using as few bits as possible. Considerable workhas been done in the past to develop both scalar and vector quantization procedures to quantize the LPC parameters [2, 3,4]. Scalar quantizers quantize each of the LPC parameters independently, while vector quantizers consider the entire set of LPC parameters as an entity and allow for direct minimization of quantization distortion. Because of this, the vector quantizers result in smaller distortion than the scalar quantizers at any given bit rate. The vector quantizers, however, have one major problem; their computational complexity is high. In our earlier paper [3], we have reported on a vector quantizer where the LPC parameter vector is split in the line spectral frequency (LSF) domain to overcome this complexity problem. We have shown that this quantizer can quantize the LPC parameters at 24 bits/frame with an average spectral distortion of 1 dB, less than 2% frames having spectral distortionl in the range 2-4 dB and no frame having spectral distortion greater than 4 dB.

A cascade formant model is well suited to describe certain speech segments, such as vowels and vowel-like sounds. The formant model is also useful because the relationship between formants and the vocal tract configurations are well understood; however, this model is not adequate for other speech sounds, such as stops, fricatives, nasals, etc. On the other hand, LPC (linear predictive coding) analysis, of sufficiently high order, can adequately describe the spectrum of any speech sound, but the relationship between the LPC parameters and the spectrum or vocal tract configuration is not obvious. This paper describes a speech analysis/synthesis scheme that uses both formants and LPC parameters for different sections of a speech signal. Thus, in some regions, the benefit of the formant model can be utilized, while in other regions, the LPC representation can be used to obtain a good description of the speech spectrum. The analysis algorithm resolves the problem of discontinuities that arise from using the two different spectral representations. When provided with the same multipulse signal, the method of speech analysis described in this paper produces resynthesized speech of the quality of multipulse LPC.

An elegant relationship (here called symmetry) between the linear predictive coding (LPC) analysis and the composite sinusoidal modeling (CSM) analysis is described. The objective is to provide a systematic view of the theory of voice spectral modeling such as LPC, PARCOR, CSM, and line spectral pairs (LSP). All of these methods of analysis are reduced to the problems of deriving the model parameters for lower‐order autocorrelation functions equal to the number of degrees of freedom of the model. From the point of view of the theory of orthogonal polynomials, LPC and CSM are the theory of orthogonal polynomials of a unit circle (in the case of LPC) and on the real axis (in the case of CSM). Beautiful symmetries hold in regard to the formulation, definition of various parameters, and solution algorithm. Also, a new interpretation is given here for the LSP from the point of view of orthogonal polynomials. © 2002 Scripta Technica, Electron Comm Jpn Pt 3, 85(6): 42–54, 2002

A device testing method called Transient Signal Analysis (TSA) is subjected to elements of a real process and testing environment in this paper. Simulation experiments are designed to determine the effects of process skew (obtained from measured parameters of a real process) on the accuracy of TSA in estimating path delays from power supply I/sub DDT/ and V/sub DDT/ waveforms. The circuit model is designed to test TSA under deep submicron process models that incorporate advanced parameters such as transistor V/sub t/ width dependencies. Modeling elements of a testing environment including the probe card are subsequently introduced as a means of evaluating the effects of tester measurement noise in an actual implementation.

In this paper, we derived a relationship between Linear Predictive Coding (LPC) coefficients and Immittance Spectral Pairs (ISP) pseudo-cepstrum. In Adaptive Multi-Rate Wideband(AMR-WB) speech codec, ISP parameters were used for quantisation and interpolation. This paper first described the representation of ISP pseudo-cepstrum. ISP pseudo-cepstrum was defined as the inverse z-transform of half natural logarithm of immittance function. References [2] and [3] proposed two kinds of new perceptual weighting filters based on ISP pseudo-cepstrum. Spectral tilt is more pronounced in wideband signals due to the wide dynamic range between low and high frequencies. The proposed filters are helpful for solving this problem. Then we gave the conversion method between ISP pseudo-cepstrum and polynomials cepstrum coefficients. Finally we derived the recursive relations between ISP pseudo-cepstrum and LPC coefficients. There are two methods of converting LPC coefficients to ISP pseudo-cepstrum. The one is directly from LPC coefficients to ISP pseudo-cepstrum. The other is from ISP parameters.

Analysis of transient signals in the diagnostic of fusion plasmas often requires the simultaneous consideration of their time and frequency information. The newly emerging technique of wavelet analysis contains both time and frequency domains. Therefore it can be a valuable tool for the analysis of transients. In this paper the basic method of wavelet analysis is described. As an example, wavelet analysis is applied to the well-known phenomena of mode locking and fishbone instability. The results quantify the current qualitative understanding of these events in terms of instantaneous frequencies and amplitudes and encourage applications of the method to other problems.

음성코딩 시 성도는 Linear Predictive Coding (LPC) 계수를 이용해서 모델링 한다. 일반적으로 LPC 계수는 양자화와 선형보간 관점에서 유리한 Line Spectral Frequency (LSF) 파라미터로 변경하여 사용한다. 10차 이상의 다차원 LSF 데이터를 벡터 양자화를 이용하여 직접 코딩하게 되면 벡터 내 상관관계 (intra-frame correlation)를 모두 이용할 수 있으므로 rate-distortion 관점에서는 높은 효율을 기대할 수 있다. 하지만, 계산량과 메모리 요구량이 높아져서 실제 코딩 시스템에서는 사용할 수 없게 되므로, 차원을 나누어 압축하는 Split Vector Quantization (SVQ)이 이용된다. 또한, LSF 데이터는 과거 벡터와의 벡터 간 상관관계 (inter-frame correlation)가 높으므로, 이를 이용한 Predictive Split Vector Quantization (PSVQ)이 사용되고 있다. PSVQ는 SVQ 보다 높은 rate-distortion 성능을 보인다. 본 논문에서는 음성 저장 장치를 위한 최적의 PSVQ를 구현하기 위해서 다수의 과거 프레임 정보와의 벡터 간상관관계 (inter-frame correlation)를 고려한 Multi-Frame AR-model 기반 SVQ (MF-AR-SVQ)를 제안하였다. 기존 PSVQ와 비교해 보았을 때, MF-AR-SVQ는 계산량과 메모리 요구량의 큰 증가 없이, 평균 spectral distortion 관점에서 약 1비트의 성능 향상을 보였다. For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

Large-scale AC-DC hybrid power systems have become a basic pattern for power transmission in China. Currently, time-domain simulation is widely used for transient stability analysis, which has the disadvantages of high computational burdens and lack of system stability margin information. An alternative method for transient stability analysis is the direct method, which assesses transient stability from the energy point of view. Direct method requires much less computation efforts. In addition, the stability margin of the system can be obtained. Due to the challenges in system modelling, energy function construction, and determination of critical energy value, it is challenging to apply direct method for transient stability analysis of AC-DC hybrid power systems. This paper is a step in exploring the application of direct method in AC-DC hybrid power systems.

An algorithm for LPC (linear predictive coding) parameter optimization in multipulse (MP)-LPC based speech coders is presented. It is shown that, by taking into account the nature of the MP-excitation signal into LPC parameter computation, it is possible to improve the effectiveness of the LPC model. This results in a better quality of the reconstructed signal in terms both of objective and subjective criteria. The implementation details of the algorithm are discussed and experimental results are presented. In particular a comparison with standard MP-LPC techniques is given. >