Download Constructing an invertible constant-Q transform with nonstationary Gabor frames An efficient and perfectly invertible signal transform featuring a constant-Q frequency resolution is presented. The proposed approach is based on the idea of the recently introduced nonstationary Gabor frames. Exploiting the properties of the operator corresponding to a family of analysis atoms, this approach overcomes the problems of the classical implementations of constant-Q transforms, in particular, computational intensity and lack of invertibility. Perfect reconstruction is guaranteed by using an easy to calculate dual system in the synthesis step and computation time is kept low by applying FFT-based processing. The proposed method is applied to real-life signals and evaluated in comparison to a related approach, recently introduced specifically for audio signals.
Download Real-Time Audio Visualization With Reassigned Non-uniform Filter Banks Filter banks, both uniform and non-uniform, are widely used for signal analysis and processing. However, the application of a timefrequency localized filter inevitably causes some amount of spectral and temporal leakage that, simultaneously, cannot be arbitrarily reduced. Reassignment is a classical procedure to eliminate this leakage in short-time Fourier spectrograms, thereby providing a sharper, more exact time-frequency domain signal representation. The reassignment technique was recently generalized to general filter banks, opening new possibilities for its application in signal analysis and processing. We present here the very first implementation of filter bank reassignment in a real-time analysis setting, more specifically as visualization in a basic audio player application. The visualization provides a low delay moving spectrogram with respect to virtually any time-frequency filter bank by interfacing the C backend of the LTFAT open-source toolbox for time-frequency processing. Low delay is achieved by blockwise processing, implemented with the JUCE C++ Library.
Download Non-Iterative Phaseless Reconstruction From Wavelet Transform Magnitude In this work, we present an algorithm for phaseless reconstruction from magnitude-only wavelet coefficients. The method relies on an explicit relation between the log-magnitude and phase gradients of analytic wavelet transforms and an extension of the Phase-Gradient Heap Integration (PGHI) algorithm recently introduced for Gabor phaseless reconstruction. This relation is exact for a certain family of mother wavelets including Cauchy wavelets of arbitrary order, but only holds approximately otherwise. The presented experiments show that, in practice, the proposed wavelet PGHI method provides competitive quality for various mother wavelets. Furthermore, wavelet PGHI is a non-iterative scheme and thus computational performance is significantly better than established alternate projection methods.
Download Accelerating Matching Pursuit for Multiple Time-Frequency Dictionaries Matching pursuit (MP) algorithms are widely used greedy methods to find K-sparse signal approximations in redundant dictionaries. We present an acceleration technique and an implementation
of the matching pursuit algorithm acting on a multi-Gabor dictionary, i.e., a concatenation of several Gabor-type time-frequency
dictionaries, consisting of translations and modulations of possibly different windows, time- and frequency-shift parameters. The
proposed acceleration is based on pre-computing and thresholding
inner products between atoms and on updating the residual directly
in the coefficient domain, i.e., without the round-trip to the signal domain. Previously, coefficient-domain residual updates have
been dismissed as having prohibitive memory requirements. By
introducing an approximate update step, we can overcome this restriction and greatly improve the performance of matching pursuit
at a modest cost in terms of approximation quality per selected
atom. An implementation in C with Matlab and GNU Octave interfaces is available, outperforming the standard Matching Pursuit
Toolkit (MPTK) by a factor of 3.5 to 70 in the tested conditions.
Additionally, we provide experimental results illustrating the convergence of the implementation.
Download LTFATPY: Towards Making a Wide Range of Time-Frequency Representations Available in Python LTFATPY is a software package for accessing the Large Time Frequency Analysis Toolbox (LTFAT) from Python. Dedicated to time-frequency analysis, LTFAT comprises a large number of linear transforms for Fourier, Gabor, and wavelet analysis along with their associated operators. Its filter bank module is a collection of computational routines for finite impulse response and band-limited filters, allowing for the specification of constant-Q and auditory-inspired transforms. While LTFAT has originally been written in MATLAB/GNU Octave, the recent popularity of the Python programming language in related fields, such as signal processing and machine learning, makes it desirable to have LTFAT available in Python as well. We introduce LTFATPY, describe its main features, and outline further developments.