Conditional split lattice vector quantization for spectral encoding of audio signals

In this paper we propose a novel quantization method with application to audio coding. Because the lattice truncation based quantizers are finite, not all input points have nearest neighbors within the defined truncations. The proposed conditional split lattice vector quantizer (CSLVQ) allows the possibility of splitting to lower dimensions an input point falling outside the truncation enabling thus the preservation of a low distortion, with only a local payoff in bitrate. Furthermore, the proposed quantization tool is versatile with respect to the dimension of the input data, the same quantization functions being used for different dimensions. The new quantizer has been tested for spectral encoding of real audio samples by encoding each frequency subband of the audio signal using a vector quantizer consisting of a lattice truncated following a generalized Gaussian contour of equiprobability. The results of objective listening tests show similar results to the AAC for high bitrates and clearly better results than the AAC for lower bitrates.