Quantization is a crucial link in the process of digital speech communication. Non-uniform quantizer such as the logarithm quantizers are commonly used in practice. In this paper, a companding non-uniform quantizer is designed using two neural networks to perform the nonlinear transformation. Particle swarm optimization is applied to find the weights of neural networks such that the signal to noise ratio (SNR) is maximized. Simulation results on different speech samples are presented and the proposed quantizer design is compared with the logarithm quantizer for bit rates ranging from 3 to 8.

Meeting Name

2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005


Electrical and Computer Engineering

Keywords and Phrases

Digital Speech Communication; Learning (Artificial Intelligence); Logarithm Quantizer; Neural Nets; Neural Network; Nonlinear Transformation; Nonuniform Scalar Quantizer Design; Particle Swarm Optimisation; Particle Swarm Optimization; Quantisation (Signal); Signal to Noise Ratio; Speech Coding

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