Adaptive wavelet-packet analysis for audio coding purposes

Abstract

This paper describes a wavelet-based perceptual audio coder, addressing the problem of the search for the wavelet-packet decomposition that minimizes a new perceptual cost function computed in the wavelet domain. We are interested in decompositions adapted to the nature of audio signals which take into account the characteristics of human hearing. The results of audio coding with three different decomposition criteria are presented for comparison purposes. They all give rise to adaptive wavelet-trees obtained minimizing different cost functions. These cost functions are the non-normalized Shannon entropy, the SUPER and our proposed perceptual cost function. Another important contribution is the algorithm for bit allocation, that takes into consideration the synthesis filter bank. The results confirm that the best way to achieve maximum compression rate and transparent coding is the usage of perceptual-entropy-based decompositions. Experimental results indicate that our coding scheme ensures transparent coding of one channel CD-quality audio signals at bit rates below 64 kbps for most audio signals.