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Many audio applications rely on filter banks (FBs) to analyze, process, andre-synthesize sounds. To approximate the auditory frequency resolution in thesignal chain, some applications rely on perceptually motivated FBs, thegammatone FB being a popular example. However, most perceptually motivated FBsonly allow partial signal reconstruction at high redundancies and/or do nothave good resistance to sub-channel processing. This paper introduces anoversampled perceptually motivated FB enabling perfect reconstruction,efficient FB design, and adaptable redundancy. The filters are directlyconstructed in the frequency domain and linearly distributed on a perceptualfrequency scale (e.g. ERB, Bark, or Mel scale). The proposed design allows forvarious filter shapes, uniform or non-uniform FB setting, and largedown-sampling factors. For redundancies $\geq$ 3 perfect reconstruction isachieved by computing the canonical dual FB analytically. For lowerredundancies perfect reconstruction is achieved using an iterative method.Experiments show performance improvements of the proposed approach whencompared to the gammatone FB in terms of reconstruction error and resistance tosub-channel processing, especially at low redundancies.

A Perceptually Motivated Filter Bank with Perfect Reconstruction for Audio Signal Processing