Linear Prediction(선형 예측)란 무엇입니까?
Linear Prediction 선형 예측 - The results show that the system can assist the management personnel to carry out construction management and improve the engineering efficiency through linear prediction. [1] Moreover, several strategies such as the use of non-uniform sampling, linear prediction, and variable recycling time were optimized to reduce the acquisition time. [2] In the second stage, a linear prediction-based parameter estimation approach with high accuracy and strong robustness is used to estimate the signal parameters of the sub-harmonics, harmonics and inter-harmonics. [3] Yet, the spectra present distinct tones that are not far from linear predictions, with the thin boundary layer cases being closer to empirical predictions. [4] Machine learning permits complex nonlinear prediction with maximum precision and efficiency. [5] To solve the difficulty in improving the lossless image compression ratio, we propose an improved lossless image compression algorithm that theoretically provides an approximately quadruple compression combining the linear prediction, integer wavelet transform (IWT) with output coefficients processing and Huffman coding. [6] However, the linear predictions under CCEθ deviate from both the initial and the experimentally measured carbon content. [7] This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. [8] The prediction gains over linear predictors are examined numerically, demonstrating the improvement of nonlinear prediction. [9] The root-mean-square (RMS) error, maximum error and jitter corresponding to the RNN prediction on the test set were smaller than the same performance measures obtained with linear prediction and least mean squares (LMS). [10] The existing prediction methods not only have great limitations on the input variables but also have many deficiencies in the nonlinear prediction. [11] Comorbidities that could worsen the patient outcome were included as linear predictions; these analyses were further broken down to the different states of the Mexican Republic and the healthcare providers within. [12] We extend the linear prediction-based dereverberation method called weighted prediction error (WPE). [13] Therefore, based on the publicly available HSI stress database, we used a Linear Prediction (LP) algorithm to select only 8 characteristic bands from the original 106 bands to generate StO2 and performed the task of identifying psychological stress and physical stress. [14] In this paper, a comparative analysis has been carried out amongst nine (DOA) algorithms: Capon, MUSIC, Bartlett, Pisarenko, Linear prediction, Maximum entropies, and Min-norm, Root-MUSIC, and ESPRIT. [15] In this work, we present a new generalized stochastic Frank–Wolfe method which closes this gap for the class of structured optimization problems encountered in statistical and machine learning characterized by empirical loss minimization with a certain type of “linear prediction” property (formally defined in the paper), which is typically present in loss minimization problems in practice. [16] The presented results imply that the confidence of the commonly used F-test of prediction improvement in the Granger causality analysis depends on the number of past values of the predicted variable included in a linear prediction. [17] Our research results provided the deeper understanding on the dynamics of combustion system in multi-point injection natural gas engines and may be beneficial to achieve nonlinear prediction and to develop improved control strategies for inhibiting the CCVs. [18] As applications, we introduced three staggered optimization algorithms: eigenvalue, match, and linear prediction; and discussed performances of the filters designed for a moving target indication (MTI) radar. [19] EPA applied a linear prediction–correction filter (LPCF) model for real-time CW, which is based on the autoregressive (AR) model. [20] The ESN model is used to obtain online a nonlinear prediction of the system free response, and a linearized version of the neural model is obtained at each sampling time to get a local approximation of the system step response, which is used to build the dynamic matrix of the system. [21] The present work proposes a method for detection of high impedance faults based on linear prediction. [22] In this paper, we proposed a linear prediction based spectral warping method by using the knowledge of vowel and non-vowel regions in speech signals to mitigate the formant frequencies differences between child and adult speakers. [23]결과는 시스템이 관리 직원이 건설 관리를 수행하고 선형 예측을 통해 엔지니어링 효율성을 개선하는 데 도움이 될 수 있음을 보여줍니다. [1] 또한, 비균일 샘플링, 선형 예측 및 가변 재활용 시간 사용과 같은 여러 전략을 최적화하여 획득 시간을 줄였습니다. [2] 두 번째 단계에서는 하위 고조파, 고조파 및 상호 고조파의 신호 매개변수를 추정하기 위해 높은 정확도와 강력한 견고성을 갖춘 선형 예측 기반 매개변수 추정 방식이 사용됩니다. [3] 그러나 스펙트럼은 선형 예측에서 멀지 않은 뚜렷한 색조를 나타내며 얇은 경계층의 경우는 경험적 예측에 더 가깝습니다. [4] 기계 학습은 최대 정밀도와 효율성으로 복잡한 비선형 예측을 허용합니다. [5] 무손실 영상 압축률 개선의 어려움을 해결하기 위해 선형 예측, 정수 웨이블릿 변환(IWT)과 출력 계수 처리 및 허프만 코딩을 결합하여 이론적으로 대략 4배 압축을 제공하는 개선된 무손실 영상 압축 알고리즘을 제안합니다. [6] 그러나 CCEθ에서 선형 예측은 초기 및 실험적으로 측정된 탄소 함량에서 벗어납니다. [7] 이 방법은 먼저 측정된 신호의 NSR(잡음 대 신호 비율)을 개선하는 선형 예측(LP) 모델을 기반으로 하는 역상관을 사용하여 잡음을 전경화합니다. [8] 선형 예측자에 대한 예측 이득은 수치적으로 조사되어 비선형 예측의 개선을 보여줍니다. [9] RMS(Root-mean-square) 오류, 최대 오류 및 테스트 세트의 RNN 예측에 해당하는 지터는 선형 예측 및 LMS(최소 평균 제곱)로 얻은 동일한 성능 측정보다 작았습니다. [10] 기존의 예측 방법은 입력 변수에 큰 한계가 있을 뿐만 아니라 비선형 예측에 많은 결함이 있습니다. [11] 환자의 결과를 악화시킬 수 있는 동반 질환은 선형 예측으로 포함되었습니다. 이러한 분석은 멕시코 공화국의 여러 주와 그 안의 의료 제공자로 더 세분화되었습니다. [12] 우리는 가중 예측 오류(WPE)라고 하는 선형 예측 기반 잔향 방법을 확장합니다. [13] 따라서 공개된 HSI 스트레스 데이터베이스를 기반으로 LP(Linear Prediction) 알고리즘을 사용하여 원래 106개 밴드 중 8개 특성 밴드만 선택하여 StO2를 생성하고 심리적 스트레스와 신체적 스트레스를 식별하는 작업을 수행했습니다. [14] 본 논문에서는 Capon, MUSIC, Bartlett, Pisarenko, Linear Prediction, Maximum entropies, Min-norm, Root-MUSIC, ESPRIT의 9가지 DOA 알고리즘을 비교 분석하였다. [15] 이 작업에서 우리는 특정 유형의 "선형 예측" 속성(공식적으로 정의됨 논문), 일반적으로 실제로 손실 최소화 문제에 존재합니다. [16] 제시된 결과는 Granger 인과관계 분석에서 일반적으로 사용되는 예측 개선 F-검정의 신뢰도가 선형 예측에 포함된 예측 변수의 과거 값의 수에 의존함을 의미합니다. [17] 우리의 연구 결과는 다점식 천연 가스 엔진에서 연소 시스템의 역학에 대한 더 깊은 이해를 제공했으며 비선형 예측을 달성하고 CCV를 억제하기 위한 개선된 제어 전략을 개발하는 데 도움이 될 수 있습니다. [18] 응용 프로그램으로서 우리는 고유값, 일치 및 선형 예측의 세 가지 시차 최적화 알고리즘을 도입했습니다. MTI(Moving Target Indicator) 레이더용으로 설계된 필터의 성능에 대해 논의했습니다. [19] EPA는 자기회귀(AR) 모델을 기반으로 하는 실시간 CW에 선형 예측-수정 필터(LPCF) 모델을 적용했습니다. [20] ESN 모델은 시스템 자유 응답의 비선형 예측을 온라인으로 얻는 데 사용되며, 신경 모델의 선형 버전은 각 샘플링 시간에 얻어서 동적 행렬을 구축하는 데 사용되는 시스템 단계 응답의 로컬 근사값을 얻습니다. 시스템의. [21] 본 연구에서는 선형 예측을 기반으로 고임피던스 결함을 탐지하는 방법을 제안합니다. [22] 본 논문에서는 아동과 성인 화자의 포먼트 주파수 차이를 완화하기 위해 음성 신호의 모음 및 비 모음 영역에 대한 지식을 사용하여 선형 예측 기반 스펙트럼 워핑 방법을 제안했습니다. [23]
mel frequency cepstral 멜 주파수
The Mel frequency cepstral coefficients (MFCCs), MFCCs with energy (MFCC_E), and perceptual linear prediction coefficients with energy (PLP_E) are utilized for feature extraction. [1] This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, as well as kurtosis parameters. [2] The feature extraction techniques such as Linear Predictive Coding Cepstral coefficients (LPCCs), Mel Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Prediction (PLPs) coefficients were applied integrating delta, delta2, and energy parameters to evaluate the performance of the proposed methodology for speaker dependent recognition. [3] From the noise removed speech signals, the spectral features like LPC (Linear Prediction Coefficients), MFCC (Mel frequency cepstral coefficients), PSD (power spectral density) and prosodic features like energy, entropy, formant frequencies and pitch are extracted and certain features are selected by ASFO (Adaptive Sunflower Optimization Algorithm). [4] Initially, different frame-level spectral techniques such as the Linear Prediction Cepstral Coefficients (LPCC), Perceptual LP coefficients (PLP), and Mel-Frequency Cepstral Coefficients (MFCC) have been examined. [5] Cepstral features such as Mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstral coefficients (LPCCs) are considered to represent timbre information. [6] Mel-frequency cepstral coefficients (MFCCs) and relAtive specTrA perceptual linear prediction (RASTA-PLP) features are evaluated independently and conjointly with two different classification techniques, random forests (RF) and deep neural networks (DNN). [7] The combination of acoustic features such as amplitude modulation spectrogram (AMS), mel-frequency cepstral coefficient (MFCC), relative spectral transformed perceptual linear prediction coefficients (RASTA-PLP) and Gammatone filter bank power spectra (GF) were used as input features to estimate target mask. [8] Mel frequency magnitude coefficient and three conventional spectral features, Mel frequency cepstral coefficient, log frequency power coefficient and linear prediction cepstral coefficient are tested on Berlin, Ravdess, Savee, EMOVO, eNTERFACE and Urdu databases with multiclass support vector machine as the classifier. [9] In order to perform the proper classification, the spectral features like spectral centroid, spectral roll-off, spectral skewness, spectral kurtosis, spectral slope, spectral crest factor, and spectral flux, and cepstral domain features like mel-frequency cepstral coefficients (MFCCs), linear prediction cepstral coefficients (LPCCs), Perceptual linear prediction (PLP) cepstral coefficients, Greenwood function cepstral coefficients (GFCC), and gammatone cepstral coefficients (GTCCs) are extracted. [10] Then, the hybrid features of SBMS/IESF and the Linear Prediction Cepstral Coefficients (LPCC)/Mel-Frequency Cepstral Coefficients (MFCC) are further studied. [11] The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. [12] Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction coefficients (LPC) can replicate human auditory system. [13] These features are the mel-frequency cepstral coefficients (MFCCs), mel-frequency spectral coefficients (MFSCs), and the perceptual linear prediction features (PLPs). [14] The selected features include Mel-frequency cepstral coefficients, Gammatone cepstral coefficients, linear prediction coefficients, spectral roll-off, and zero-crossing rate. [15] For investigating the presence of speaker and language-specific information, spectral features like Mel frequency cepstral coefficients (MFCCs), shifted delta cepstral (SDC), and relative spectral transform-perceptual linear prediction (RASTA-PLP) features are used here. [16] This automatic speech recognition system using the MFCC method (Mel frequency cepstral coefficient) and a LPC method (linear prediction coding) for the representation of the speech signal, and SVM (support vector machine), for speech recognition. [17] In this study, we evaluated the mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction coefficients (PLP) as a feature extraction step to improve the accuracy for paved and unpaved road classification. [18] One of the methods is Mel frequency cepstral coefficient (MFCC) and other is linear prediction cepstral coefficients (LPCC). [19]멜 주파수 셉스트럴 계수(MFCC), 에너지가 있는 MFCC(MFCC_E) 및 에너지가 있는 지각 선형 예측 계수(PLP_E)는 특징 추출에 사용됩니다. [1] 이 작업은 MFCC(멜 주파수 셉스트럴 계수) 및 LPCC(선형 예측 셉스트럼 계수), 왜도를 사용하여 노인 음성 신호를 감지하기 위한 FNN(피드포워드 신경망) 및 CNN(컨볼루션 신경망)과 같은 딥 러닝 방법에 중점을 둡니다. , 뿐만 아니라 첨도 매개변수. [2] nan [3] nan [4] 처음에는 LPCC(Linear Prediction Cepstral Coefficients), PLP(Perceptual LP coefficients), MFCC(Mel-Frequency Cepstral Coefficients)와 같은 다양한 프레임 수준 스펙트럼 기술이 조사되었습니다. [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19]
support vector machine 지원 벡터 기계
Based on the calculation results, support vector machine is used to solve the linear prediction problem of traffic carbon emissions. [1] Finally, the adjusted E-BLSP feature and other two traditional features, including linear prediction cepstrum coefficient (LPCC) and mel-frequency cepstrum coefficients (MFCC) are applied to support vector machine (SVM) and deep neural network (DNN) classifiers to explore the classification performance of single feature and feature combinations for pathological and normal vowels /a/, /i/ and /u/. [2]계산 결과를 기반으로 지원 벡터 머신을 사용하여 교통 탄소 배출량의 선형 예측 문제를 해결합니다. [1] nan [2]
frequency cepstral coefficient 주파수 중심 계수
The paper investigates the performance of perceptual based speech features like Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficient and Inverted Mel Frequency Cepstral Coefficients on SER. [1] Acoustic predictors of the perceived vowel quality included the harmonics-to-noise ratio (HNR), smoothed cepstral peak prominence (CPP), recurrence period density entropy (RPDE), Gammatone frequency cepstral coefficients (GFCCs), linear prediction (LP) coefficients and their variants, and modulation spectrogram features. [2]이 논문은 SER에서 Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficients and Inverted Mel Frequency Cepstral Coefficients와 같은 지각 기반 음성 기능의 성능을 조사합니다. [1] 인지된 모음 품질의 음향 예측 변수에는 HNR(harmonics-to-noise ratio), CPP(smoothed cepstral peak prominence), RPDE(recurring period density entropy), GFCC(감마톤 주파수 셉스트럴 계수), LP(선형 예측) 계수 및 변형 및 변조 스펙트로그램 기능. [2]
Perceptual Linear Prediction 지각적 선형 예측
The Mel frequency cepstral coefficients (MFCCs), MFCCs with energy (MFCC_E), and perceptual linear prediction coefficients with energy (PLP_E) are utilized for feature extraction. [1] The paper investigates the performance of perceptual based speech features like Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficient and Inverted Mel Frequency Cepstral Coefficients on SER. [2] The feature extraction techniques such as Linear Predictive Coding Cepstral coefficients (LPCCs), Mel Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Prediction (PLPs) coefficients were applied integrating delta, delta2, and energy parameters to evaluate the performance of the proposed methodology for speaker dependent recognition. [3] Mel-frequency cepstral coefficients (MFCCs) and relAtive specTrA perceptual linear prediction (RASTA-PLP) features are evaluated independently and conjointly with two different classification techniques, random forests (RF) and deep neural networks (DNN). [4] Automatic Speaker Identification (ASI) is a biometric technique, which had achieved reliability in real applications, with standard feature extraction methods such as Linear Predictive Cepstral Coefficients (LPCC), Perceptual Linear Prediction (PLP), and modeling methods such as Gaussian mixture model (GMM), etc. [5] The combination of acoustic features such as amplitude modulation spectrogram (AMS), mel-frequency cepstral coefficient (MFCC), relative spectral transformed perceptual linear prediction coefficients (RASTA-PLP) and Gammatone filter bank power spectra (GF) were used as input features to estimate target mask. [6] In order to perform the proper classification, the spectral features like spectral centroid, spectral roll-off, spectral skewness, spectral kurtosis, spectral slope, spectral crest factor, and spectral flux, and cepstral domain features like mel-frequency cepstral coefficients (MFCCs), linear prediction cepstral coefficients (LPCCs), Perceptual linear prediction (PLP) cepstral coefficients, Greenwood function cepstral coefficients (GFCC), and gammatone cepstral coefficients (GTCCs) are extracted. [7] These features are the mel-frequency cepstral coefficients (MFCCs), mel-frequency spectral coefficients (MFSCs), and the perceptual linear prediction features (PLPs). [8] For investigating the presence of speaker and language-specific information, spectral features like Mel frequency cepstral coefficients (MFCCs), shifted delta cepstral (SDC), and relative spectral transform-perceptual linear prediction (RASTA-PLP) features are used here. [9] In this study, we evaluated the mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction coefficients (PLP) as a feature extraction step to improve the accuracy for paved and unpaved road classification. [10]멜 주파수 셉스트럴 계수(MFCC), 에너지가 있는 MFCC(MFCC_E) 및 에너지가 있는 지각 선형 예측 계수(PLP_E)는 특징 추출에 사용됩니다. [1] 이 논문은 SER에서 Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficients and Inverted Mel Frequency Cepstral Coefficients와 같은 지각 기반 음성 기능의 성능을 조사합니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10]
Simple Linear Prediction 단순 선형 예측
The first subpopulation is created by a simple linear prediction model with two different stepsizes. [1] Using the simple linear prediction model, table eggs purchased randomly from retail egg shops had quality values of eggs that were about 4 - 11 days after lay prior to purchase. [2] Experimental results show that our proposed approach improves label prediction quality, in terms of precision and nDCG, by 3% to 5% in three of the 5 tasks and is competitive in the others, even with a simple linear prediction model. [3]첫 번째 부분 모집단은 두 개의 다른 단계 크기를 가진 간단한 선형 예측 모델에 의해 생성됩니다. [1] 단순 선형 예측 모델을 사용하여 소매 계란 가게에서 무작위로 구입한 식탁용 계란의 품질 값은 구입 전 산란 후 약 4~11일이었습니다. [2] nan [3]
Multichannel Linear Prediction 다채널 선형 예측
The presented blind dereverberation method consists in multichannel linear prediction (MCLP) and enforces sparsity of the dereverberated speech by adopting the split Bregman approach. [1] To address these drawbacks, this letter proposes to decompose the multichannel linear prediction filter as a Kronecker product of a temporal (interframe) prediction filter and a spatial filter. [2] We extend the state-of-the-art online dereverberation method, online weighted prediction error (WPE), which predicts late reverberation components using a multichannel linear prediction (MCLP) filter. [3]제시된 블라인드 잔향 제거 방법은 다중 채널 선형 예측(MCLP)으로 구성되며 분할 Bregman 접근 방식을 채택하여 잔향이 제거된 음성의 희소성을 적용합니다. [1] 이러한 단점을 해결하기 위해 이 편지에서는 다중 채널 선형 예측 필터를 시간(프레임간) 예측 필터와 공간 필터의 Kronecker 곱으로 분해할 것을 제안합니다. [2] nan [3]
Domain Linear Prediction 도메인 선형 예측
We propose a technique to compute spectrograms using Frequency Domain Linear Prediction (FDLP) that uses all-pole models to fit the squared Hilbert envelope of speech in different frequency sub-bands. [1] The sub-band envelopes are derived using frequency domain linear prediction (FDLP) which performs an autoregressive estimation of the Hilbert envelopes. [2] Discrete All Pole, Frequency Domain Linear Prediction, Low Pass Filter, and True envelopes are firstly studied and applied to the noise excitation signal in our continuous vocoder. [3]우리는 전극 모델을 사용하여 서로 다른 주파수 하위 대역에서 음성의 제곱 힐베르트 엔벨로프를 맞추는 주파수 영역 선형 예측(FDLP)을 사용하여 스펙트로그램을 계산하는 기술을 제안합니다. [1] 부대역 엔벨로프는 힐베르트 엔벨로프의 자기회귀 추정을 수행하는 FDLP(주파수 영역 선형 예측)를 사용하여 파생됩니다. [2] nan [3]
Excited Linear Prediction
This paper proposes a novel method to reduce the order of prediction filter from 10 to 7 in Code Excited Linear Prediction (CELP) coding framework by the inclusion of psychoacoustic Mel scale into Linear Predictive Coding (Mel-LPC). [1] This work consists on using packet loss concealment methods based on Multiple Description Coding (MDC) and Forward Error Correction (FEC) to improve speech quality deterioration caused by packet losses for Code-Excited Linear Prediction (CELP) based coders in packet network. [2]본 논문에서는 Mel-LPC(Linear Predictive Coding)에 심리음향 Mel scale을 포함하여 CELP(Code Excited Linear Prediction) 코딩 프레임워크에서 예측 필터의 차수를 10에서 7로 줄이는 새로운 방법을 제안합니다. [1] nan [2]
Channel Linear Prediction
The system is based on the algorithm including multi-channel linear prediction and adaptive linear prediction. [1] Autoregressive modelling techniques such as multi-channel linear prediction are widely used for applications such as coding, dereverberation and compression of the speech signals. [2]시스템은 다중 채널 선형 예측 및 적응 선형 예측을 포함하는 알고리즘을 기반으로 합니다. [1] nan [2]
Combine Linear Prediction
To guarantee that the tracking errors of all system state variables converge to zero in finite time and eliminate the chattering phenomenon caused by the switching control action, a control strategy that combines linear prediction model of disturbances and fuzzy sliding mode control (SMC) based on logical framework with side conditions (LFSC) was designed. [1] We are implementing reversible steganography that combines linear prediction error value coding and histogram shifting. [2]모든 시스템 상태 변수의 추적 오차가 유한한 시간 내에 0으로 수렴되도록 보장하고 스위칭 제어 동작으로 인한 채터링 현상을 제거하기 위해, 논리 기반의 외란 선형 예측 모델과 퍼지 슬라이딩 모드 제어(SMC)를 결합한 제어 전략 측면 조건이 있는 프레임워크(LFSC)가 설계되었습니다. [1] nan [2]
Marginal Linear Prediction 한계 선형 예측
We provide a novel methodological approach that allows us to measure the relative contribution of happiness to SLE, by combining the Shapley–Owen–Shorrocks decomposition with contrasts of marginal linear predictions of the equality of the means by groups. [1] We develop a model which takes into account endogeneity problems and uses contrasts of marginal linear predictions. [2]우리는 Shapley-Owen-Shorrocks 분해를 그룹별 평균 평등에 대한 한계 선형 예측의 대조와 결합하여 SLE에 대한 행복의 상대적 기여도를 측정할 수 있는 새로운 방법론적 접근 방식을 제공합니다. [1] 우리는 내생성 문제를 고려하고 한계 선형 예측의 대조를 사용하는 모델을 개발합니다. [2]
Use Linear Prediction 선형 예측 사용
We use linear prediction filter technique to reconstruct the substorm-related response of electron densities at different altitudes and ionospheric conductances from long-term observations made by the European Incoherent SCATer (EISCAT) radar located at Tromso. [1] In this letter, a novel reconstruction-based adaptive beamformer is proposed, which uses linear prediction to generate virtual sensor data and extend array aperture. [2]우리는 선형 예측 필터 기술을 사용하여 Tromso에 위치한 EISCAT(European Incoherent SCATer) 레이더에 의해 수행된 장기간 관찰에서 다양한 고도 및 전리층 컨덕턴스에서 전자 밀도의 서브스톰 관련 응답을 재구성합니다. [1] 이 편지에서는 선형 예측을 사용하여 가상 센서 데이터를 생성하고 어레이 조리개를 확장하는 새로운 재구성 기반 적응형 빔 형성기가 제안됩니다. [2]
Adaptive Linear Prediction 적응 선형 예측
Owing to the slow varying characteristic of atmospheric turbulence, we used an adaptive linear prediction (ALP) filter to predict the decision threshold of the upcoming frame. [1] In order to reduce energy consumption, this article designs an adaptive linear prediction algorithm. [2]대기 난류의 느린 변화 특성으로 인해 적응 선형 예측(ALP) 필터를 사용하여 다가오는 프레임의 결정 임계값을 예측했습니다. [1] 에너지 소비를 줄이기 위해 이 기사에서는 적응형 선형 예측 알고리즘을 설계합니다. [2]
Unbiased Linear Prediction 편향되지 않은 선형 예측
In this tutorial, we present the case, with the accompanying statistical theory, for estimating the study specific true effects using so called 'empirical Bayes estimates' or 'Best Unbiased Linear Predictions' under the random‐effects model. [1] To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV’s for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. [2]이 자습서에서는 무작위 효과 모델에서 소위 '경험적 베이즈 추정' 또는 '최상의 편향되지 않은 선형 예측'을 사용하여 연구의 특정 실제 효과를 추정하는 경우를 통계 이론과 함께 제시합니다. [1] GP를 평가하기 위해 Genomic Unbiased Linear Prediction(GBLUP), Ridge Regression(RR), Least Absolute Shrinkage and Selection Operator(LASSO), Elastic Net(EN), 패널에서 APR에 대한 GEBV를 황색, 잎 및 줄기 녹으로 추정하기 위한 베이지안 능선 회귀(BRR), 베이지안 A(BA), 베이지안 B(BB), 베이지안 C(BC) 및 재생 커널 힐베르트 간격 모델(RKHS) 363개의 아프가니스탄 원산지 빵 밀 토지. [2]
linear prediction model 선형 예측 모델
The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. [1] Autoregressive models in image processing are linear prediction models that split an image into a predicted (i. [2] This study combines the proposed partial least squares–based multivariate adaptive regression spline (PLS–MARS) method and a regional multi-variable associate rule mining and Rank–Kennard-Stone method (MVARC-R-KS) to construct a nonlinear prediction model to realize local optimality considering spatial heterogeneity. [3] Furthermore, the trend for land use and carbon stock was estimated post 2018 using a linear prediction model. [4] The first subpopulation is created by a simple linear prediction model with two different stepsizes. [5] Subsequently, we exploit a linear combination of the subarray manifolds to formulate forward/backward linear prediction models. [6] Considering all 6 input variables, the linear prediction model performs well and can be used if all input parameters are measured with a tomographer. [7] A linear prediction model was constructed for comparison by selecting anatomical predictors of past literature. [8] Aiming at performance improvement, we propose here the adoption of information sharing in multi-task learning frameworks, involving linear and nonlinear prediction models, and time series with and without differentiation. [9] In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. [10] Inside each time slot, traditional linear prediction model is then adopted and a TPR-Tree is built to support spatio-temporal queries. [11] The linear prediction model and big data analysis method are used to sample and process the communication network signals, and the abnormal signals in the signal acquisition results are identified. [12] Therefore, the linear prediction model has limitations when modeling multiple human samples. [13] The results of spatial changes allowed successful development of accurate linear prediction models for moisture content as a function of area shrinkage (on scaled variables) with excellent prediction capability (|BIAS|. [14] Relative cumulative eluotropic strength was introduced as a novel descriptor in building a linear prediction model, which not only solves the problem that acylcarnitines with long carbon chains cannot be well predicted in traditional models but also proves its robustness and transferability across instruments in two data sets that were acquired in distinct chromatography conditions. [15] The investigated processing algorithms are based on the linear prediction model, i. [16] Using the simple linear prediction model, table eggs purchased randomly from retail egg shops had quality values of eggs that were about 4 - 11 days after lay prior to purchase. [17] This paper introduces a Model Predictive Control (MPC) based hydro turbine's governor load/frequency controller whose linear prediction model parameters are updated depending on the operating point. [18] An end-to-end network is designed to convert the speech linear prediction model in KF to non-linear, and to compact all other conventional linear filtering operations. [19] The linear information on latitude and longitude at the current timestep is forecast by combining the AR model with the trajectory output from the AM to achieve a combination of linear and nonlinear prediction models. [20] There has been a consensus of cross-component prediction in video compression that the interchannel redundancies can be effectively removed through a linear prediction model. [21] Linear prediction models may be inadequate to describe the process at all operating points. [22] According to the engineering application results, the real crack generation time is consistent with that of our estimation method, which demonstrates that our nonlinear prediction model and method for fatigue life are effective. [23] It further builds a multi-linear prediction model to forecast the Agriculture Sector’s economic performance in terms of GDP and NPAs generated by the Agricultural Sector using Machine Learning Techniques. [24] On average, the most favorable out-of-sample performance is obtained via a two-stage procedure, where a conventional linear prediction model is fitted first and the boosting technique is applied to build a nonlinear model for its residuals. [25] According to the nonlinearity of the energy system, this paper uses the principle of the grey nonlinear prediction model NGBM(1,1) to improve the background value of the model, and by the simulated annealing algorithm, we put forward the optimized grey nonlinear model ONGBM(1,1). [26] To guarantee that the tracking errors of all system state variables converge to zero in finite time and eliminate the chattering phenomenon caused by the switching control action, a control strategy that combines linear prediction model of disturbances and fuzzy sliding mode control (SMC) based on logical framework with side conditions (LFSC) was designed. [27] We selected a log-linear prediction model, resulting in a mean case-fatality ratio of 2·2% (95% CI 0·7–4·5) in 1990–2015. [28] The training of the adaptive bit count predictor is based on a linear prediction model that uses either coefficient-wise average entropy or a ρ-parameter. [29] Experimental results show that our proposed approach improves label prediction quality, in terms of precision and nDCG, by 3% to 5% in three of the 5 tasks and is competitive in the others, even with a simple linear prediction model. [30] The multivariate nonlinear prediction model built in this study can provide guidance for the design and selection of such spraying dust suppression schemes as nozzle outlet diameter, feed water pressure and nozzle layout, etc. [31]일일 ED 방문의 예측 가능성은 단계적 선택을 사용하여 선형 예측 모델을 생성하여 조사되었습니다. 평균 절대 백분율 오차(MAPE)는 적합도의 측정으로 사용되었습니다. [1] 이미지 처리의 자기회귀 모델은 이미지를 예측된 이미지로 분할하는 선형 예측 모델입니다(i. [2] 이 연구는 제안된 부분 최소 자승 기반 다변수 적응 회귀 스플라인(PLS-MARS) 방법과 지역 다변수 연관 규칙 마이닝 및 Rank-Kennard-Stone 방법(MVARC-R-KS)을 결합하여 다음과 같은 비선형 예측 모델을 구성합니다. 공간적 이질성을 고려한 지역 최적성을 실현합니다. [3] 또한 선형 예측 모델을 사용하여 2018년 이후 토지 이용 및 탄소 축적량 추세를 추정했습니다. [4] 첫 번째 부분 모집단은 두 개의 다른 단계 크기를 가진 간단한 선형 예측 모델에 의해 생성됩니다. [5] nan [6] 6개의 입력 변수를 모두 고려하면 선형 예측 모델이 잘 수행되며 모든 입력 매개변수를 단층 촬영기로 측정하면 사용할 수 있습니다. [7] 비교를 위해 과거 문헌의 해부학적 예측변수를 선택하여 선형 예측 모델을 구성했습니다. [8] 성능 향상을 목표로 여기에서 선형 및 비선형 예측 모델, 미분 유무에 관계없이 시계열을 포함하는 다중 작업 학습 프레임워크에서 정보 공유의 채택을 제안합니다. [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] 단순 선형 예측 모델을 사용하여 소매 계란 가게에서 무작위로 구입한 식탁용 계란의 품질 값은 구입 전 산란 후 약 4~11일이었습니다. [17] nan [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26] 모든 시스템 상태 변수의 추적 오차가 유한한 시간 내에 0으로 수렴되도록 보장하고 스위칭 제어 동작으로 인한 채터링 현상을 제거하기 위해, 논리 기반의 외란 선형 예측 모델과 퍼지 슬라이딩 모드 제어(SMC)를 결합한 제어 전략 측면 조건이 있는 프레임워크(LFSC)가 설계되었습니다. [27] nan [28] nan [29] nan [30] nan [31]
linear prediction coefficient 선형 예측 계수
The Mel frequency cepstral coefficients (MFCCs), MFCCs with energy (MFCC_E), and perceptual linear prediction coefficients with energy (PLP_E) are utilized for feature extraction. [1] The paper investigates the performance of perceptual based speech features like Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficient and Inverted Mel Frequency Cepstral Coefficients on SER. [2] From the noise removed speech signals, the spectral features like LPC (Linear Prediction Coefficients), MFCC (Mel frequency cepstral coefficients), PSD (power spectral density) and prosodic features like energy, entropy, formant frequencies and pitch are extracted and certain features are selected by ASFO (Adaptive Sunflower Optimization Algorithm). [3] Here, the features such as Taylor-based delta AMS, Holoentropy, fluctuation index, relative energy, tonal power ratio, spectral features, and linear prediction coefficient (LPC) are acquired from each channel. [4] It can be subdivided into steganography and steganalysis based on FBC (fixed codebook), LPC (linear prediction coefficient), and ACB (adaptive codebook). [5] Important steps of calculating nonlinear prediction coefficients are carried out to assist the input signals. [6] The combination of acoustic features such as amplitude modulation spectrogram (AMS), mel-frequency cepstral coefficient (MFCC), relative spectral transformed perceptual linear prediction coefficients (RASTA-PLP) and Gammatone filter bank power spectra (GF) were used as input features to estimate target mask. [7] The performance of speech coding, speech recognition, and speech enhancement largely depends upon the accuracy of the linear prediction coefficient (LPC) of clean speech and noise in practice. [8] , time characteristics (segmentation, window types, and classification regions—lengths and overlaps), frequency ranges, frequency scales, processing of whole speech (spectrograms), vocal tract (filter banks, linear prediction coefficient (LPC) modeling), and excitation (inverse LPC filtering) signals, magnitude and phase manipulations, cepstral features, etc. [9] The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. [10] Current deep learning approaches to linear prediction coefficient (LPC) estimation for the augmented Kalman filter (AKF) produce bias estimates, due to the use of a whitening filter. [11] Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction coefficients (LPC) can replicate human auditory system. [12] Current augmented Kalman filter (AKF)-based speech enhancement algorithms utilise a temporal convolutional network (TCN) to estimate the clean speech and noise linear prediction coefficient (LPC). [13] The selected features include Mel-frequency cepstral coefficients, Gammatone cepstral coefficients, linear prediction coefficients, spectral roll-off, and zero-crossing rate. [14] Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. [15] In Kalman filter (KF)-based speech enhancement, each clean speech frame is represented by an auto-regressive (AR) process, whose parameters comprise the linear prediction coefficients (LPCs) and prediction error variance. [16] The inaccurate estimates of linear prediction coefficient (LPC) and noise variance 1 introduce bias in Kalman filter (KF) gain and degrades speech enhancement performance. [17] In this study, we evaluated the mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction coefficients (PLP) as a feature extraction step to improve the accuracy for paved and unpaved road classification. [18] Necessary steps including the computation of linear prediction coefficients have been taken to accommodate the input signal. [19]멜 주파수 셉스트럴 계수(MFCC), 에너지가 있는 MFCC(MFCC_E) 및 에너지가 있는 지각 선형 예측 계수(PLP_E)는 특징 추출에 사용됩니다. [1] 이 논문은 SER에서 Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficients and Inverted Mel Frequency Cepstral Coefficients와 같은 지각 기반 음성 기능의 성능을 조사합니다. [2] nan [3] 여기서 Taylor 기반 델타 AMS, Holoentropy, 변동 지수, 상대 에너지, 톤 전력 비율, 스펙트럼 특성 및 선형 예측 계수(LPC)와 같은 특성을 각 채널에서 획득합니다. [4] FBC(고정 코드북), LPC(선형 예측 계수), ACB(적응 코드북)를 기반으로 스테가노그래피와 스테가분석으로 나눌 수 있습니다. [5] 비선형 예측 계수를 계산하는 중요한 단계는 입력 신호를 지원하기 위해 수행됩니다. [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19]
linear prediction cepstral 선형 예측
This study used Linear Prediction Cepstral Coefficients (LPCC) and Spectral Centroid Magnitude Cepstral Coefficients (SCMC) features along with log-Mel band energies for the representation of an acoustic scene. [1] Initially, different frame-level spectral techniques such as the Linear Prediction Cepstral Coefficients (LPCC), Perceptual LP coefficients (PLP), and Mel-Frequency Cepstral Coefficients (MFCC) have been examined. [2] Cepstral features such as Mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstral coefficients (LPCCs) are considered to represent timbre information. [3] Mel frequency magnitude coefficient and three conventional spectral features, Mel frequency cepstral coefficient, log frequency power coefficient and linear prediction cepstral coefficient are tested on Berlin, Ravdess, Savee, EMOVO, eNTERFACE and Urdu databases with multiclass support vector machine as the classifier. [4] In order to perform the proper classification, the spectral features like spectral centroid, spectral roll-off, spectral skewness, spectral kurtosis, spectral slope, spectral crest factor, and spectral flux, and cepstral domain features like mel-frequency cepstral coefficients (MFCCs), linear prediction cepstral coefficients (LPCCs), Perceptual linear prediction (PLP) cepstral coefficients, Greenwood function cepstral coefficients (GFCC), and gammatone cepstral coefficients (GTCCs) are extracted. [5] Then, the hybrid features of SBMS/IESF and the Linear Prediction Cepstral Coefficients (LPCC)/Mel-Frequency Cepstral Coefficients (MFCC) are further studied. [6] The proposed method is implemented in MATLAB, and it will be contrasted with the existing method such as Linear Prediction Cepstral Coefficient (LPCC) with the K-Nearest Neighbour (KNN) classifier to test the samples for optimal performance evaluation. [7] Then, two characteristic parameter extraction methods are analyzed in detail, including linear prediction cepstral coefficient and Mel cepstral coefficient. [8] One of the methods is Mel frequency cepstral coefficient (MFCC) and other is linear prediction cepstral coefficients (LPCC). [9]이 연구에서는 음향 장면을 표현하기 위해 log-Mel 밴드 에너지와 함께 선형 예측 셉스트럴 계수(LPCC) 및 스펙트럼 중심 크기 셉스트럴 계수(SCMC) 기능을 사용했습니다. [1] 처음에는 LPCC(Linear Prediction Cepstral Coefficients), PLP(Perceptual LP coefficients), MFCC(Mel-Frequency Cepstral Coefficients)와 같은 다양한 프레임 수준 스펙트럼 기술이 조사되었습니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9]
linear prediction coding 선형 예측 코딩
In order to improve pronunciation, it is proposed to adapt the linear prediction coding coefficients of reference sounds by using the gradient descent optimization of the gain-optimized dissimilarity. [1] In the preprocessing stage, features extraction is done by using both the linear prediction coding (LPC) technique for coding the spectrograms, and a waveform parameterization for characterizing amplitude characteristics in the time domain, for each of the three components. [2] This paper introduces a new time-resolved spectral analysis method based on the Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of EEG (Electroencephalography) activity. [3] The analysis covered 220 signals acquired from real experiment and pre-processed with the use of power spectral density estimation (PSD) and linear prediction coding (LPC). [4] This automatic speech recognition system using the MFCC method (Mel frequency cepstral coefficient) and a LPC method (linear prediction coding) for the representation of the speech signal, and SVM (support vector machine), for speech recognition. [5] The proposed solution is based on linear prediction coding of the ECG extracted features. [6] ” For feature extraction, linear prediction coding (LPC) of discrete wavelet transform (DWT) subsignals denoted by LPCW was used. [7]발음을 향상시키기 위해 이득 최적화 비유사도의 경사하강법 최적화를 이용하여 참조음의 선형 예측 부호화 계수를 적응시키는 것을 제안한다. [1] 전처리 단계에서 특징 추출은 스펙트로그램을 코딩하기 위한 선형 예측 코딩(LPC) 기술과 세 가지 구성요소 각각에 대해 시간 영역에서 진폭 특성을 특성화하기 위한 파형 매개변수화를 모두 사용하여 수행됩니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7]
linear prediction method 선형 예측 방법
To baseline the performance, comparative experimental validation between the proposed NSO and a widely used linear prediction method is conducted on a 12/8 SRM setup. [1] However, powerful nonlinear prediction methods such as deep learning and SVM suffer from interpretability problem, making it hard to use in domains where the reason for decision making is required. [2] Features of the target echoes are extracted by linear prediction method and wavelet analysis methods, and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted. [3] In this paper, a new linear prediction method is proposed. [4] The linear prediction methods rely on the stability of environment and time series, so they cannot completely simulate the complex nonlinear fluctuations characteristics of hotel passenger flow. [5] This study aims to compare linear and nonlinear prediction methods in predicting the Kovats retention index of 126 compounds extracted from the Lippia origanoides plant. [6] In the tracking module, the insensitivity of texture features to light changes is used, and the improved mean shift algorithm of adaptive linear fusion of color and texture features combined with linear prediction method are applied to track spherical markers continuously under illumination changes. [7]성능을 기준으로 하기 위해 제안된 NSO와 널리 사용되는 선형 예측 방법 간의 비교 실험 검증이 12/8 SRM 설정에서 수행됩니다. [1] 그러나 딥 러닝, SVM과 같은 강력한 비선형 예측 방법은 해석 가능성 문제가 있어 의사 결정의 이유가 필요한 영역에서 사용하기 어렵습니다. [2] 대상 에코의 특징은 선형 예측 방법과 웨이블릿 분석 방법으로 추출되며 선형 예측 계수와 선형 예측 켑스트럼 계수도 추출됩니다. [3] nan [4] nan [5] nan [6] nan [7]
linear prediction cepstrum 선형 예측 켑스트럼
This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, as well as kurtosis parameters. [1] Along with GFP features, the Linear Prediction Cepstrum Co-efficient (LFCC) and statistical parameters are computed. [2] The features extracted from emotional speech samples that make up the database for the speech emotion recognition system include power, pitch, linear prediction cepstrum coefficient (LPCC), and Mel frequency cepstrum coefficient (MFCC). [3] Then, the voiceprint features of the collected acoustic signal are extracted with the Mel Frequency Cepstrum Coefficients (MFCC) and the Linear Prediction Cepstrum Coefficients (LPCC). [4] Finally, the adjusted E-BLSP feature and other two traditional features, including linear prediction cepstrum coefficient (LPCC) and mel-frequency cepstrum coefficients (MFCC) are applied to support vector machine (SVM) and deep neural network (DNN) classifiers to explore the classification performance of single feature and feature combinations for pathological and normal vowels /a/, /i/ and /u/. [5]이 작업은 MFCC(멜 주파수 셉스트럴 계수) 및 LPCC(선형 예측 셉스트럼 계수), 왜도를 사용하여 노인 음성 신호를 감지하기 위한 FNN(피드포워드 신경망) 및 CNN(컨볼루션 신경망)과 같은 딥 러닝 방법에 중점을 둡니다. , 뿐만 아니라 첨도 매개변수. [1] GFP 기능과 함께 LFCC(Linear Prediction Cepstrum Co-efficient) 및 통계 매개변수가 계산됩니다. [2] nan [3] nan [4] nan [5]