Neural Networks Based Non-Uniform Scalar Quantizer Design with Particle Swarm Optimization
This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1769
There were 8 downloads as of 28 Jun 2016.
Abstract
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.
This paper has been withdrawn.
