A new scheme for robust, high-quality, embedded speech coding based on subband decomposition and perceptually optimized bit allocation and prioritization is presented. An infinite impulse response (IIR) quadrature mirror filterbank (QMF) performs subband decomposition. A perceptual model, computed using subband spectral analysis, optimizes the coder's perceptual quality. Dynamic bit allocation and prioritization is combined with embedded quantization resulting in little performance degradation relative to a nonembedded implementation. The coder output is scalable from high quality at higher bit rates to lower quality at lower bit rates, supporting a wide range of service and resource utilization. The lower bit-rate representation is obtained simply through truncation of the higher bit-rate representation. Since source-rate adaptation is performed through truncation of the encoded stream, interaction with the coder is not required, making the embedded coder ideally suited for rate-adaptive communication systems. Performance for both speech and music was verified through subjective listening tests.

The goal of this study is to develop a robust and high-quality speech coder for wireless communication. The proposed coder is a perceptually-based variable-rate subband coder. The perceptual metric ensures that encoding is optimized to the human listener and is based on calculating the signal-to-mask ratio in short-time frames of the input signal. An adaptive bit allocation scheme is employed and the subband energies are then quantized using a Max-Lloyd quantizer. The coder is fully scalable-increasing the bit rates, improves the quality of encoded speech. Subjective listening tests, using quiet and noisy input signals, indicate that the proposed coder produces high-quality speech when operating at 12 kbps or higher. In error-free conditions, our coder has comparable performance to that of QCELP or GSM coders. For speech in background noise, however, our coder, at 12 kbps, outperforms QCELP significantly, and for music, it outperforms both QCELP and GSM.

A methodology for transform-based spectral analysis of subband filtered signals is developed. The methodology is based on performing the analysis on subband samples instead of on the input signal directly. Aliasing due to decimation is eliminated by including the effects of the adjacent subband in the analysis of frequencies near the filterbank transition regions. The frequency resolution and spectral leakage is nearly the same as if the transform had been performed on the input directly. In an M band filterbank, the analysis block length is reduced by a factor of M. This reduces the complexity of source compression techniques based on subband decomposition and spectral analysis.