An optimization framework for fixed-point digital signal processing.

Lam Yuet Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 80-86).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.1.1 --- Difficulties of fixed-point design --- p.1Chapter 1.1.2 --- Why still fixed-point? --- p.2Chapter 1.1.3 --- Difficulties of converting floating-point to fixed-point --- p.2Chapter 1.1.4 --- Why wordlength optimization? --- p.3Chapter 1.2 --- Objectives --- p.3Chapter 1.3 --- Contributions --- p.3Chapter 1.4 --- Thesis Organization --- p.4Chapter 2 --- Review --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- Simulation approach to address quantization issue --- p.6Chapter 2.3 --- Analytical approach to address quantization issue --- p.8Chapter 2.4 --- Implementation of speech systems --- p.9Chapter 2.5 --- Discussion --- p.10Chapter 2.6 --- Summary --- p.11Chapter 3 --- Fixed-point arithmetic background --- p.12Chapter 3.1 --- Introduction --- p.12Chapter 3.2 --- Fixed-point representation --- p.12Chapter 3.3 --- Fixed-point addition/subtraction --- p.14Chapter 3.4 --- Fixed-point multiplication --- p.16Chapter 3.5 --- Fixed-point division --- p.18Chapter 3.6 --- Summary --- p.20Chapter 4 --- Fixed-point class implementation --- p.21Chapter 4.1 --- Introduction --- p.21Chapter 4.2 --- Fixed-point simulation using overloading --- p.21Chapter 4.3 --- Fixed-point class implementation --- p.24Chapter 4.3.1 --- Fixed-point object declaration --- p.24Chapter 4.3.2 --- Overload the operators --- p.25Chapter 4.3.3 --- Arithmetic operations --- p.26Chapter 4.3.4 --- Automatic monitoring of dynamic range --- p.27Chapter 4.3.5 --- Automatic calculation of quantization error --- p.27Chapter 4.3.6 --- Array supporting --- p.28Chapter 4.3.7 --- Cosine calculation --- p.28Chapter 4.4 --- Summary --- p.29Chapter 5 --- Speech recognition background --- p.30Chapter 5.1 --- Introduction --- p.30Chapter 5.2 --- Isolated word recognition system overview --- p.30Chapter 5.3 --- Linear predictive coding processor --- p.32Chapter 5.3.1 --- The LPC model --- p.32Chapter 5.3.2 --- The LPC processor --- p.33Chapter 5.4 --- Vector quantization --- p.36Chapter 5.5 --- Hidden Markov model --- p.38Chapter 5.6 --- Summary --- p.40Chapter 6 --- Optimization --- p.41Chapter 6.1 --- Introduction --- p.41Chapter 6.2 --- Simplex Method --- p.41Chapter 6.2.1 --- Initialization --- p.42Chapter 6.2.2 --- Reflection --- p.42Chapter 6.2.3 --- Expansion --- p.44Chapter 6.2.4 --- Contraction --- p.44Chapter 6.2.5 --- Stop --- p.45Chapter 6.3 --- One-dimensional optimization approach --- p.45Chapter 6.3.1 --- One-dimensional optimization approach --- p.46Chapter 6.3.2 --- Search space reduction --- p.47Chapter 6.3.3 --- Speeding up convergence --- p.48Chapter 6.4 --- Summary --- p.50Chapter 7 --- Word Recognition System Design Methodology --- p.51Chapter 7.1 --- Introduction --- p.51Chapter 7.2 --- Framework design --- p.51Chapter 7.2.1 --- Fixed-point class --- p.52Chapter 7.2.2 --- Fixed-point application --- p.53Chapter 7.2.3 --- Optimizer --- p.53Chapter 7.3 --- Speech system implementation --- p.54Chapter 7.3.1 --- Model training --- p.54Chapter 7.3.2 --- Simulate the isolated word recognition system --- p.56Chapter 7.3.3 --- Hardware cost model --- p.57Chapter 7.3.4 --- Cost function --- p.58Chapter 7.3.5 --- Fraction size optimization --- p.59Chapter 7.3.6 --- One-dimensional optimization --- p.61Chapter 7.4 --- Summary --- p.63Chapter 8 --- Results --- p.64Chapter 8.1 --- Model training --- p.64Chapter 8.2 --- Simplex method optimization --- p.65Chapter 8.2.1 --- Simulation platform --- p.65Chapter 8.2.2 --- System level optimization --- p.66Chapter 8.2.3 --- LPC processor optimization --- p.67Chapter 8.2.4 --- One-dimensional optimization --- p.68Chapter 8.3 --- Speeding up the optimization convergence --- p.71Chapter 8.4 --- Optimization criteria --- p.73Chapter 8.5 --- Summary --- p.75Chapter 9 --- Conclusion --- p.76Chapter 9.1 --- Search space reduction --- p.76Chapter 9.2 --- Speeding up the searching --- p.77Chapter 9.3 --- Optimization criteria --- p.77Chapter 9.4 --- Flexibility of the framework design --- p.78Chapter 9.5 --- Further development --- p.78Bibliography --- p.8

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Structure out of sound

Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1993.Vita.Includes bibliographical references (p. 155-170).Michael Jerome Hawley.Ph.D

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Mathematical linguistics

but in fact this is still an early draft, version 0.56, August 1 2001. Please d