Super-Wideband Spectral Envelope Modeling for Speech Coding

Abstract

Significant improvements in the quality of speech coders have been achieved by widening the coded frequency range from narrowband to wideband. However, existing speech coders still employ a limited band source-filter model extended by parametric coding of the higher band. In the present work, a superwideband source-filter model running at 32 kHz is considered and especially its spectral magnitude envelope modeling. To match super-wideband operating mode, we adapted and compared two methods; Linear Predictive Coding (LPC) and Distribution Quantization (DQ). LPC uses autoregressive modeling, while DQ quantifies the energy ratios between different parts of the spectrum. Parameters of both methods were quantized with a multi-stage vector quantization. Objective and subjective evaluations indicate that both methods used in a super-wideband source-filter coding scheme offer the same quality range, making them an attractive alternative to conventional speech coders that require additional bandwidth extension.