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

Abstract

Featured Application: The proposed framework is highly suitable for audio applications that require analysis-synthesis systems with the following properties: stability, perfect reconstruction, and a flexible choice of redundancy.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 minimized to avoid audible artifacts. Other advantageous properties include stability and low redundancy. To exploit some aspects of 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, referred to as Audlet, is 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.

Perceptually Controlled Reshaping of Sound Histograms

“…Future works will focus on reducing the complexity factor due to the length of the coloring filter impulse response. The solution could be based on a perception-based filter-bank (auditory filters mimicking the auditory system [30], [31]) and white noise addition in each frequency channel, which would avoid the memory effect responsible for complexity.…”

Application of frame multipliers for the extraction of curve squeals from train signals

“…Wichtig dabei ist, dass der Multiplikator auf die vollen komplexen STFT-Koeffizienten angewandt wird. Durch ihre Interpretation und Verallgemeinerung im Konzept von Rahmen können in der LTFAT auch andere Zeit-Frequenz-Darstellungen verwendet werden, etwa Wavelets [11], eine konstant-Q-Transformation [13] oder auch perzeptive Filterbänke [16].…”

Characterization of Analytic Wavelet Transforms and a New Phaseless Reconstruction Algorithm

“…Inversion can be achieved by interpreting the wavelet transform as a filter bank analysis and invoking the frame theory of uniform filter banks [61]- [64] to compute a dual filter bank synthesis. This can be done either directly using dual filters ψ k , or iteratively [9], [65] using conjugate gradient iterations. The consideration of general uniform filter banks is necessary: It is not always possible to find a dual filter bank, which is required to achieve perfect reconstruction, with wavelet structure.…”

“…Time-frequency and time-scale representations are fundamental tools in many areas of signal analysis and signal processing, ranging from medical data [1], [2], damage or fault detection in materials [3] and machines [4], to image [5], [6] and audio processing [7]- [9]. Such representations are usually complex-valued and admit a natural decomposition into magnitude and phase components, with the phase containing crucial information about the analyzed signal.…”