PERCEPTUAL SUBBAND AUDIO CODING USING ADAPTIVE MULTITYPE SPARSE VECTOR QUANTIZATION, AND SIGNAL SATURATION SCALER

摘要

A reduced-complexity real-time audio coder/decoder ('codec') using adaptive sparse vector quantization (ASVQ) classifies signal vectors into one of a plurality of different types. The codec provides low bit-rate compression for music and speech, while being applicable to higher bit-rate audio compression. It includes an in-path implementation of psychoacoustic spectral masking, and frequency-domain quantization using ASVQ and audio-compression algorithms. Critically-sampled subband filter banks maintain time-domain continuity across frame boundaries. In-path spectral masking usually results in sparse vectors. ASVQ is particularly effective for quantizing sparse signal vectors. In the preferred embodiment, ASVQ classifies signal vectors into six different types (504, 508, 512, 516, 520, and 522). The ASVQ technique applies to general-purpose data quantization as well as to quantization for audio compression. The invention also includes a 'soft-clipping' algorithm in the decoder as a post-processing stage, which preserves the waveform shapes of the reconstructed time-domain audio signal in a frame- or block-oriented stateless manner while maintaining continuity across frame or block boundaries.