Spectral quantization by companding

Recent advances in spectral coding for speech suggest Gaussian mixture modeling (GMM) as a tool for designing close to optimal, single stage quantizers. This combined with companding techniques promises to give flexible, no-memory methods that are essentially rate universal. We present methods for designing high performance companding structures based on a random union of Z-lattices. With this choice of basic lattice, VQ search is brought to the level of scalar quantization. OUT results show performance that clearly outperforms conventional VQ companding. OUT results are discussed in terms of the shape of the Vornoi cells of the overall quantizer. We apply the overall procedure to predictive spectrum quantization in two scenarios in terms of two audio bandwidths. For 16 kHz sampling we reach a spectral distortion (SD) of 1 at 33 bits per frame.