With the advent of 3G Wireless standards and subsequent bandwidth expansion, there is a clear need to design high quality, low complexity compression schemes which are bit-efficient. We have proposed a computationally efficient, high quality, vector quantization scheme based on a parametric probability density function (PDF). In this scheme, speech line spectral frequencies (LSF) are modeled as i.i.d realizations of a multivariate Gaussian mixture density. The mixture model parameters are efficiently estimated using the Expectation Maximization (EM) algorithm. An efficient quantization scheme using transform coding and bit allocation techniques which allows for easy and computationally efficient mapping from observation to quantized value is developed for both fixed rate and variable rate systems. An attractive feature of this method is that source encoding using the resultant codebook involves very few searches and its computational complexity is minimal and independent of the rate of the system. Furthermore, the proposed scheme is bit scalable and can switch between memoryless and quantizer with memory seamlessly. The performance of the memoryless quantizer is 2-3 bits better than conventional quantization schemes. 1
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.