| Paper: | SLP-P12.9 | ||
| Session: | Speech Processing for Reverberation, Quantization and Enhancement | ||
| Time: | Thursday, May 18, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Wide-band Speech Coding | ||
| Title: | Efficient Quantization of Statistically Normalized Vectors using Multi-Option Partial-Order Bit-Assignment Schemes | ||
| Authors: | Sean Ramprashad, DoCoMo Communications Laboratories, USA, United States | ||
| Abstract: | In this paper we focus on new options for the efficient quantization of statistically normalized target vectors at low bitrates. This problem is fundamental to many low-rate speech and audio coder designs. Here many such coders follow a general principle of taking a structured speech or audio signal, applying a process of redundancy removal and then quantizing each of the resulting statistically normalized targets to a relevant distortion level. We look at this latter problem when some of these targets are to be quantized at very low bitrates (<= 1 bit/target-scalar). The approach we take is to efficiently communicate a target-adaptive pattern of unequal bit-assignments (noise allocations) across each target. This can increase performance over an approach that has a constant noise allocation even when target vectors consist of independent and identically distributed (i.i.d.) scalars. We extend these schemes to multi-option schemes allowing further options to adapt and improve performance. | ||
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