Comparison of Nonuniform Optimal Quantizer Designs for Speech Coding With Adaptive Critics and Particle Swarm
- 29 January 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industry Applications
- Vol. 43 (1), 238-244
- https://doi.org/10.1109/TIA.2006.885897
Abstract
This paper presents the design of a companding nonuniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs and particle swarm optimization, aiming to maximize the signal-to-noise ratio. The comparison of these optimal quantizer designs over a bit-rate range of 3-6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality standardKeywords
This publication has 6 references indexed in Scilit:
- Particle swarm optimizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Navigation of mobile sensors using PSO and embedded PSO in a fuzzy logic controllerPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Adaptive-critic-based optimal neurocontrol for synchronous generators in a power system using MLP/RBF neural networksIEEE Transactions on Industry Applications, 2003
- Particle swarm optimization: developments, applications and resourcesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- The particle swarm - explosion, stability, and convergence in a multidimensional complex spaceIEEE Transactions on Evolutionary Computation, 2002
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989