V. Ramasubramanian and K. K. Paliwal
Reducing the complexity of the
LPC vector quantizer using the K-d
tree search algorithm
Abstract:Linear predictive coding (LPC) parameters are widely used in various speech coding applications for representing the spectral envelope information of speech. Transparent quantization of
the LPC parameters (average spectral distortion of 1 dB) can be achieved at 24 bits/frame using
the split vector LPC quantizer (SVLPC) which quantizes 10-dimensional line spectral frequency
(LSF) vectors in two parts. However, SVLPC suffers from a high computational complexity in
quantizing each part (one of dimension 4 and the other of dimension 6) using independent codebooks of size 4096 (corresponding to a rate of 12 bits/part). This limits the practical real-time
application of the coder. In this paper, we reduce the computational complexity of the split vector quantizer by 2 orders of magnitude using the fast K-dimensional (K-d) tree search algorithm
under the bucket-Voronoi intersection (BVI) search framework. This is of significant importance
in rendering the SVLPC amenable for practical real-time coding applications.