A new audio coding scheme using a forward masking model and perceptually weighted vector quantization

Abstract

This paper presents a new audio coder that includes two techniques to improve the sound quality of the audio coding system. First, a forward masking model is proposed. This model exploits adaptation of the peripheral sensory and neural elements in the auditory system, which is often deemed as the cause of forward masking. In the proposed audio coder, the forward masking is first modeled by a nonlinear analog circuit and then difference equations for finding the solution of this circuit are formulated. The parameters of the circuit are derived from several factors, including time difference between masker and maskee, masker level, masker frequency, and masker duration. Inclusion of this model in the coding process will remove more redundancy inaudible to humans and thus improves coding efficiency. Secondly, we propose a new vector quantization technique, whose codebooks are generated by a perceptually weighted binary-tree self-organizing feature maps (PW-BTSOFM) algorithm. This vector quantization technique adopts a perceptually weighted error criterion to train and select codewords so that the quantization error is kept below the just-noticed distortion (JND) while using the smallest possible codebook, again reducing the required coded bit rate. Experimental objective and subjective sound quality measurements show that the proposed audio coding scheme requires about 30% less bits than the MPEG layer III audio coding standard.