This paper presents the design of a companding non-uniform 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 (ACD) and particle swarm optimization (PSO), aiming to maximize the signal to noise ratio (SNR). The comparison of these optimal quantizer designs over bit rate range of 3 to 6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union''s Perceptual Evaluation of Speech Quality (PESQ) standard.

Meeting Name

40th IAS Annual Meeting of the Industry Applications Conference, 2005


Electrical and Computer Engineering

Keywords and Phrases

International Telecommunication Unions Perceptual Evaluation; PSO; SNR; Speech Quality Standard; Adaptive Codes; Adaptive Critic Design; Hearing; Neural Nets; Neural Network; Nonlinear Transformation; Nonuniform Optimal Scalar Quantizer; Particle Swarm Optimisation; Particle Swarm Optimization; Perceptual Quality; Quantisation (Signal); Signal to Noise Ratio; Speech Coding; Transform Coding

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2005