Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Linear Predictive Detection for Power Line Communications Impaired by Colored Noise
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Warped Linear Prediction of Physical Model Excitations with Applications in Audio Compression and Instrument Synthesis
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Adaptive Linear Prediction Filtering in DWT Domain for Real-Time Musical Onset Detection
This lecture introduces us to the topic of supervised learning. Here the data consists of input-output pairs. Inputs are also often referred to as covariates, predictors and features; while outputs are known as variates, targets and labels.
We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. ...
Efﬁcient decoding for syntactic parsing has become a necessary research area as statistical grammars grow in accuracy and size and as more NLP applications leverage syntactic analyses. We review prior methods for pruning and then present a new framework that uniﬁes their strengths into a single approach. Using a log linear model, we learn the optimal beam-search pruning parameters for each CYK chart cell, effectively predicting the most promising areas of the model space to explore.
This paper presents a novel application of Alternating Structure Optimization (ASO) to the task of Semantic Role Labeling (SRL) of noun predicates in NomBank. ASO is a recently proposed linear multi-task learning algorithm, which extracts the common structures of multiple tasks to improve accuracy, via the use of auxiliary problems. In this paper, we explore a number of different auxiliary problems, and we are able to signiﬁcantly improve the accuracy of the NomBank SRL task using this approach.
Linear Prediction Modelling of Speech Linear predictive models are widely used in speech processing applications such as low–bit–rate speech coding in cellular telephony, speech enhancement and speech recognition. Speech is generated by inhaling air into the lungs, and then exhaling it through the vibrating glottis cords and the vocal tract. The random, noise-like, air flow from the lungs is spectrally shaped and amplified by the vibrations of the glottal cords and the resonance of the vocal tract.
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This thesis addresses two neural network based control systems. The ﬁrst is a neural network based predictive controller. System identiﬁcation and controller design are discussed. The second is a direct neural network controller. Parameter choice and training methods are discussed. Both controllers are tested on two diﬀerent plants. Problems regarding implementations are discussed. First the neural network based predictive controller is introduced as an extension to the generalised predictive controller (GPC) to allow control of non-linear plant.
6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 Wiener Filters: Least Square Error Estimation Block-Data Formulation of the Wiener Filter Interpretation of Wiener Filters as Projection in Vector Space Analysis of the Least Mean Square Error Signal Formulation of Wiener Filters in the Frequency Domain Some Applications of Wiener Filters The Choice of Wiener Filter Order Summary
iener theory, formulated by Norbert Wiener, forms the foundation of data-dependent linear least square error filters.
CHANNEL EQUALIZATION AND BLIND DECONVOLUTION
15.1 15.2 15.3 15.4 15.5 15.6 15.7 Introduction Blind-Deconvolution Using Channel Input Power Spectrum Equalization Based on Linear Prediction Models Bayesian Blind Deconvolution and Equalization Blind Equalization for Digital Communication Channels Equalization Based on Higher-Order Statistics Summary
lind deconvolution is the process of unravelling two unknown signals that have been convolved.
Introduction to Adaptive Filters
18.1 18.2 18.3 18.4 18.5 What is an Adaptive Filter? The Adaptive Filtering Problem Filter Structures The Task of an Adaptive Filter Applications of Adaptive Filters
System Identiﬁcation • Inverse Modeling • Linear Prediction • Feedforward Control General Form of Adaptive FIR Algorithms • The MeanSquared Error Cost Function • The Wiener Solution • The Method of Steepest Descent • The LMS Algorithm • Other Stochastic Gradient Algorithms • Finite-Precision Effects and Other Implementation Issues • System Identiﬁcation Example
LINEAR PREDICTION MODELS
Linear Prediction Coding Forward, Backward and Lattice Predictors Short-term and Long-Term Linear Predictors MAP Estimation of Predictor Coefficients Sub-Band Linear Prediction Signal Restoration Using Linear Prediction Models Summary
inear prediction modelling is used in a diverse area of applications, such as data forecasting, speech coding, video coding, speech recognition, model-based spectral analysis, model-based interpolation, signal restoration, and impulse/step event detection.
Introduction Least-Squares Estimation Properties of Estimators Best Linear Unbiased Estimation Maximum-Likelihood Estimation Mean-Squared Estimation of Random Parameters Maximum A Posteriori Estimation of Random Parameters The Basic State-Variable Model State Estimation for the Basic State-Variable Model
Prediction • Filtering (the Kalman Filter) • Smoothing
Jerry M. Mendel
University of Southern California
15.10 Digital Wiener Filtering 15.11 Linear Prediction in DSP, and Kalman Filtering 15.12 Iterated Least Squares 15.