A Computationally Efficient Cochlear Filter Bank for Perceptual Audio Coding

Many applications in auditory modeling require analysis filters that approximate the frequency selectivity given by psychophysical data, e.g. from masking experiments using narrow-band maskers. This frequency selectivity is largely determined by the spectral decomposition process inside the human cochlea. Curently used spectral decomposition schemes for masking modeling in audio coding generally do not achieve the non-uniform time and frequency resolution provided by the cochlea. These applications rather take advantage of the computational efficiency of uniform filter banks or transforms at the expense of coding gain. This paper presents a suitable analysis filter-bank structure employing cascaded low-order IIR filters and appropriate down-sampling to increase efficiency. In an application example, the filter responses were optimized to model auditory masking effects. The results show that the time and frequency resolution of the filter bank matches or exceeds the masking properties. Thus, the filter bank enables improved masking modeling for audio coding at low computational costs.