This letter proposes an LSP quantizing method which uses interframe correlation of the parameters. The quantized parameters are represented as a moving average of code vectors. Using this method, LSP parameters are quantized efficiently and the degradation of decoded parameters caused by bit errors affects only a few following frames.

A quick evaluation method is proposed to obtain stability robustness measures in polynomial coefficient space based on knowledge of coefficients of a Hurwitz stable nominal polynomial. Two norms are employed: l- and l2-norm, which correspond to the stability hypercube and hyperball in the space, respectively. Just inverting Hurwitz matrix for the nominal polynomial immediately yields closed-form estimates for the size of the hypercube and hyperball.

This paper proposes a lossless scalable audio coding scheme and quality enhancement processing at the decoder to compensate for some missing scalable units of information. The bit rate scalability is achieved by combining high-compression coding, such as MPEG-4, and horizontal bit slicing of the PCM-coded error signal between the original waveform and the locally reconstructed MPEG-4 signal. The horizontally sliced stream may be transported through an IP network with priority. Even if some units are missing at the decoder, reasonable quality waveform can be reconstructed by means of preserving the important packets. In addition, quality enhancement procedures including scale adjustment and post-processing have been proposed. The scale adjustment eliminates unnecessary zero's, and the post-processing recovers the spectral envelope characteristics of the original input signal. As a result of objective quality evaluation, the two techniques are confirmed to be useful for quality enhancement when lower priority packets are lost. This scheme enables graceful degradation by supporting lossless, near lossless, and high-compression coding within a single scalable framework, and is useful for narrowband to broadband audio streaming.

This paper presents extended-domain Golomb (XDG) code, an extension of Golomb code for sparse geometric sources as well as a generalization of extended-domain Golomb-Rice (XDGR) code, based on the idea of almost instantaneous fixed-to-variable length (AIFV) codes. Showing that the XDGR encoding can be interpreted as extended usage of the code proposed in the previous works, this paper discusses the following two facts: The proposed XDG code can be constructed as an AIFV code relating to Golomb code as XDGR code does to Rice code; XDG and Golomb codes are symmetric in the sense of relative redundancy. The proposed XDG code can be efficiently used for losslessly compressing geometric sources too sparse for the conventional Golomb and Rice codes. According to the symmetry, its relative redundancy is guaranteed to be as low as Golomb code compressing non-sparse geometric sources. Awing to this fact, the parameter of the proposed XDG code, which is more finely tunable than the conventional XDGR code, can be optimized for given inputs using the conventional techniques. Therefore, it is expected to be more useful for many coding applications that deal with geometric sources at low bit rates.