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Audlet Filter Banks: A Versatile Analysis/Synthesis Framework using Auditory Frequency Scales

Thibaud Necciari, Nicki Holighaus, Peter Balazs, Zdeněk Průša, Piotr Majdak, and Olivier Derrien.

Abstract: Many audio applications rely on filter banks (FBs) to analyze, process, and re-synthesize sounds. For these applications, an important property of the analysis-synthesis system is the reconstruction error; it has to be kept to a minimum to avoid audible artifacts. Other advantageous properties include stability and low redundancy. To exploit some aspects of human auditory perception in the signal chain, some applications rely on FBs that approximate the frequency analysis performed in the auditory periphery, the gammatone FB being a popular example. However, current gammatone FBs only allow partial reconstruction and stability at high redundancies. In this article, we construct an analysis-synthesis system for audio applications. The proposed system, named Audlet, is based on an oversampled FB with filters distributed on auditory frequency scales. It allows perfect reconstruction for a wide range of FB settings (e.g., the shape and density of filters), efficient FB design, and adaptable redundancy. In particular, we show how to construct a gammatone FB with perfect reconstruction. Experiments demonstrate performance improvements of the proposed gammatone FB when compared to current gammatone FBs in terms of reconstruction error and stability, especially at low redundancies. An application of the framework to audio source separation illustrates its utility for audio processing.

Sound examples for the source separation experiment: click on a system's acronym to hear the corresponding reconstruction.
Reference signals: original mixture -- target

Rt β = 1 β = 1/6 1024-point STFT
1.1 trev_gfb Audlet_gfb Audlet_hann trev_gfb Audlet_gfb Audlet_hann STFT_hann
1.5 trev_gfb Audlet_gfb Audlet_hann trev_gfb Audlet_gfb Audlet_hann STFT_hann
4.0 trev_gfb Audlet_gfb Audlet_hann trev_gfb Audlet_gfb Audlet_hann STFT_hann

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