'Model order' diaporamas de présentation

Feature Extraction for ASR

Feature Extraction for ASR. Spectral (envelope) Analysis. Auditory Model/ Normalizations. Deriving the envelope (or the excitation). excitation. Time-varying filter. h t (n). e(n). y(n)=e(n)*h t (n). HOW CAN WE GET e(n) OR h(n) from y(n)?. But first, why?.

472 views • 45 slides

Robust control

Robust control. Saba Rezvanian. Fall-Winter 88. Robust control. A control system is robust if it is insensitive to differences between the actual system and the model of the system which was used to design the controller. Robust control. Uncertainty Noise Disturbances.

2.95k views • 44 slides

Stability and Passivity of the Super Node Algorithm for EM modelling of ICs

Stability and Passivity of the Super Node Algorithm for EM modelling of ICs. Maria Ugryumova Supervisor : Wil Schilders Technical University Eindhoven, The Netherlands. CASA day, 13 November 2008. Outline. 1. Motivation 2. Fasterix and EM simulation 3. System parameters

404 views • 40 slides

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Perceptual Linear Predictive Analysis of Speech Hynek Hermansky, Speech Technology Laboratory, J. Acoustical Society of America, April 1990. ?? : ???. Outline. Linear Prediction Coding Mel-scale Frequency Cepstral Coefficients Perceptual Linear Predictive. Introduction. Feature Extraction

617 views • 31 slides

Workshop on Directed Functional Connectivity Analysis Using Wiener-Granger Causality

Workshop on Directed Functional Connectivity Analysis Using Wiener-Granger Causality. Dr. Steven Bressler Cognitive Neurodynamics Laboratory Center for Complex Systems & Brain Sciences Department of Psychology Florida Atlantic University http:// www.ccs.fau.edu /~ bressler /.

316 views • 31 slides

Timing Verification of VLSI Circuits

Timing Verification of VLSI Circuits. Professor Weiping Shi http://ece.tamu.edu/~wshi/689.html. I. Introduction. What is EDA? Technology trend Design flow Timing verification flow. Technology Trend. International Technology Roadmap for Semiconductors (ITRS)

1.02k views • 24 slides

Scalable Symbolic Model Order Reduction

Scalable Symbolic Model Order Reduction. Yiyu Shi*, Lei He* and C. J. Richard Shi + *Electrical Engineering Department, UCLA + Electrical Engineering Department, University of Washington, Seattle

345 views • 21 slides

D etector C haracterization R obot (Progress Since January 2001)

Design (Already exists) Algorithmic and Statistical studies (In progress) Algorithm development and fast prototyping in MATLAB Soumya Mohanty, Soma Mukherjee. Computational Implementaion issues (C++) Software Environment (Standalone) Hardware requirements

231 views • 17 slides

Lecture 4. Learning (I): Approximation Theory

Lecture 4. Learning (I): Approximation Theory. Outline. What is Learning? Supervised Learning and Function Approximation Bias and Variance Trade-offs Model Selection and Sampling Strategy. What is Learning?.

307 views • 14 slides

LECTURE 12: PARAMETRIC AND MINIMUM VARIANCE SPECTRAL ESTIMATION. Objectives: AR Spectral Estimation MA Spectral Estimation ARMA Spectral Estimation Minimum Variance Estimation

116 views • 11 slides