A masking-threshold-adapted weighting filter for excitation search

Abstract

Most LPC-based audio coders improve reproduction quality by using predictor coefficients to embody perceptual masking in noise spectral shaping. Since the predictor coefficients were originally derived to characterize sound production models, they cannot precisely describe the human ear's nonlinear responses to frequency and loudness. In this paper, we report on new approaches to exploiting the masking threshold in the design of a perceptual noise-weighting filter for excitation searches. To track the nonstationary evolution of a masking threshold, an autoregressive spectral analysis with finite order has been shown to be capable of providing sufficient accuracy. In seeking faster response, an artificial neural network was also trained to extract autoregressive modeling parameters of the masking threshold from typical audio signals via mapping. Furthermore, we propose the concept of sinusoidal excitation representation to better track the intrinsic characteristics of prediction error signals. Simulation results indicate that the combined use of a multisinusoid excitation model and a masking-threshold-adapted weighting filter allows the implementation of an LPC-based audio coder that delivers near transparent quality at the rate of 96 kb/s.