Perceptual modelling for low-rate audio coding
Christopher R. Cave · eScholarship@McGill (McGill) · 2002
Sophisticated audio coding paradigms incorporate human perceptual effects in order to reduce data rates, while maintaining high fidelity of the reconstructed signal. Auditory masking is the phenomenon that is the key to exploiting perceptual redundancy in audio signals. Most auditory models conservatively estimate masking, as they were developed for medium to high rate coders where distortion can be made inaudible. At very low coding rates, more accurate auditory models will be beneficial since some audible distortion is inevitable. This thesis focuses on the application of human perception to low-rate audio coding. A novel auditory model that estimates masking levels is proposed. The new model is based on a study of existing perceptual literature. Among other features, it represents transient masking effects by tracking the temporal evolution of masking components. Moreover, an innovative bit allocation algorithm is developed that considers the excitation of quantization noise in the allocation process. The new adaptive allocation scheme is applicable with any auditory model that is based on the excitation pattern model of masking.