Reducing the complexity of the LPC vector quantizer using the k-d tree search algorithm

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 su ers 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 signi cant importance in rendering the SVLPC amenable for practical real-time coding applications.