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Nicki Holighaus

Publications and source records attributed to Nicki Holighaus.

33 records · Page 2Linked to original sources

Schur-type Banach modules of integral kernels acting on mixed-norm Lebesgue spaces

Schur's test states that if $K:X\times Y\to\mathbb{C}$ satisfies $\int_Y |K(x,y)|dν(y)\leq C$ and $\int_X |K(x,y)|dμ(x)\leq C$, then the associated integral operator acts boundedly on $L^p$ for all $p\in [1,\infty]$. We derive a variant of this result ensuring boundedness on the (weighted) mixed-norm Lebesgue spaces $L_w^{p,q}$ for all $p,q\in [1,\infty]$. For non-negative integral kernels our criterion is sharp; i.e., it is satisfied if and only if the integral operator acts boundedly on all of the mixed-norm Lebesgue spaces. Motivated by this criterion, we introduce solid Banach modules $\mathcal{B}_m(X,Y)$ of integral kernels such that all kernels in $\mathcal{B}_m(X,Y)$ map $L_w^{p,q}(ν)$ boundedly into $L_v^{p,q}(μ)$ for all $p,q \in [1,\infty]$, provided that the weights $v,w$ are $m$-moderate. Conversely, if $\mathbf{A}$ and $\mathbf{B}$ are solid Banach spaces for which all kernels $K\in\mathcal{B}_m(X,Y)$ map $\mathbf{A}$ into $\mathbf{B}$, then $\mathbf{A}$ and $\mathbf{B}$ are related to mixed-norm Lebesgue-spaces; i.e., $\left(L^1\cap L^\infty\cap L^{1,\infty}\cap L^{\infty,1}\right)_v\hookrightarrow\mathbf{B}$ and $\mathbf{A}\hookrightarrow\left(L^1 + L^\infty + L^{1,\infty} + L^{\infty,1}\right)_{1/w}$ for certain weights $v,w$ depending on the weight $m$. The kernel algebra $\mathcal{B}_m(X,X)$ is particularly suited for applications in (generalized) coorbit theory: Usually, a host of technical conditions need to be verified to guarantee that coorbit space theory is applicable for a given continuous frame $Ψ$ and a Banach space $\mathbf{A}$. We show that it is enough to check that certain integral kernels associated to $Ψ$ belong to $\mathcal{B}_m(X,X)$; this ensures that the coorbit spaces $\operatorname{Co}_Ψ(L_κ^{p,q})$ are well-defined for all $p,q\in [1,\infty]$ and all weights $κ$ compatible with $m$.

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GACELA -- A generative adversarial context encoder for long audio inpainting

We introduce GACELA, a generative adversarial network (GAN) designed to restore missing musical audio data with a duration ranging between hundreds of milliseconds to a few seconds, i.e., to perform long-gap audio inpainting. While previous work either addressed shorter gaps or relied on exemplars by copying available information from other signal parts, GACELA addresses the inpainting of long gaps in two aspects. First, it considers various time scales of audio information by relying on five parallel discriminators with increasing resolution of receptive fields. Second, it is conditioned not only on the available information surrounding the gap, i.e., the context, but also on the latent variable of the conditional GAN. This addresses the inherent multi-modality of audio inpainting at such long gaps and provides the option of user-defined inpainting. GACELA was tested in listening tests on music signals of varying complexity and gap durations ranging from 375~ms to 1500~ms. While our subjects were often able to detect the inpaintings, the severity of the artifacts decreased from unacceptable to mildly disturbing. GACELA represents a framework capable to integrate future improvements such as processing of more auditory-related features or more explicit musical features.

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A class of warped filter bank frames tailored to non-linear frequency scales

A method for constructing non-uniform filter banks is presented. Starting from a uniform system of translates, generated by a prototype filter, a non-uniform covering of the frequency axis is obtained by composition with a warping function. The warping function is a $\mathcal C^1$-diffeomorphism that determines the frequency progression and can be chosen freely, apart from minor technical restrictions. The resulting functions are interpreted as filter frequency responses. Combined with appropriately chosen decimation factors, a non-uniform analysis filter bank is obtained. Classical Gabor and wavelet filter banks are special cases of the proposed construction. Beyond the state-of-the-art, we construct a filter bank adapted to a frequency scale derived from human auditory perception and families of filter banks that can be interpreted as an interpolation between linear (Gabor) and logarithmic (wavelet) frequency scales. We derive straightforward conditions on the prototype filter decay and the decimation factors, such that the resulting warped filter bank forms a frame. In particular, a simple and constructive method for obtaining tight frames with bandlimited filters is derived by invoking previous results on generalized shift-invariant systems.

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Characterization of Analytic Wavelet Transforms and a New Phaseless Reconstruction Algorithm

We obtain a characterization of all wavelets leading to analytic wavelet transforms (WT). The characterization is obtained as a by-product of the theoretical foundations of a new method for wavelet phase reconstruction from magnitude-only coefficients. The cornerstone of our analysis is an expression of the partial derivatives of the continuous WT, which results in phase-magnitude relationships similar to the short-time Fourier transform (STFT) setting and valid for the generalized family of Cauchy wavelets. We show that the existence of such relations is equivalent to analyticity of the WT up to a multiplicative weight and a scaling of the mother wavelet. The implementation of the new phaseless reconstruction method is considered in detail and compared to previous methods. It is shown that the proposed method provides significant performance gains and a great flexibility regarding accuracy versus complexity. Additionally, we discuss the relation between scalogram reassignment operators and the wavelet transform phase gradient and present an observation on the phase around zeros of the WT.

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Adversarial Generation of Time-Frequency Features with application in audio synthesis

Time-frequency (TF) representations provide powerful and intuitive features for the analysis of time series such as audio. But still, generative modeling of audio in the TF domain is a subtle matter. Consequently, neural audio synthesis widely relies on directly modeling the waveform and previous attempts at unconditionally synthesizing audio from neurally generated invertible TF features still struggle to produce audio at satisfying quality. In this article, focusing on the short-time Fourier transform, we discuss the challenges that arise in audio synthesis based on generated invertible TF features and how to overcome them. We demonstrate the potential of deliberate generative TF modeling by training a generative adversarial network (GAN) on short-time Fourier features. We show that by applying our guidelines, our TF-based network was able to outperform a state-of-the-art GAN generating waveforms directly, despite the similar architecture in the two networks.

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Designing Gabor windows using convex optimization

Redundant Gabor frames admit an infinite number of dual frames, yet only the canonical dual Gabor system, constructed from the minimal l2-norm dual window, is widely used. This window function however, might lack desirable properties, e.g. good time-frequency concentration, small support or smoothness. We employ convex optimization methods to design dual windows satisfying the Wexler-Raz equations and optimizing various constraints. Numerical experiments suggest that alternate dual windows with considerably improved features can be found.

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Inpainting of long audio segments with similarity graphs

We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e. the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.

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Discretization in generalized coorbit spaces: extensions, annotations and errata for "Continuous Frames, Function Spaces and the Discretization Problem" by M. Fornasier and H. Rauhut

During the process of writing the manuscript ["Continuous warped time-frequency representations - Coorbit spaces and discretization", N. Holighaus, C. Wiesmeyr and P. Balazs], the work ["Continuous Frames, Function Spaces and the Discretization Problem" by M. Fornasier and H. Rauhut - (1)] was one of the major foundations of our results and, naturally, we found ourselves going back to reading that contribution once and again. In particular in Section 5, which is concerned with the discretization problem, we have found some typographical errors, small inaccuracies and some parts that we just would have wished to be slightly more accessible. Finally, for our own theory, a generalization of a central definition required us to verify that all the derivations in Section 5 of (1) still hold after the necessary modifications. Considering the importance of the results in (1) for the community, this was reason enough to start this side project of re-writing that least accessible portion of Fornasier and Rauhut's manuscript, eliminating the errors we found, adding annotations where we consider them useful and modifying the results to take into account the generalization we require for our own work. What you see is the result of our endeavor, a one-to-one substitute for Section 5 in (1).

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Frame Theory for Signal Processing in Psychoacoustics

This review chapter aims to strengthen the link between frame theory and signal processing tasks in psychoacoustics. On the one side, the basic concepts of frame theory are presented and some proofs are provided to explain those concepts in some detail. The goal is to reveal to hearing scientists how this mathematical theory could be relevant for their research. In particular, we focus on frame theory in a filter bank approach, which is probably the most relevant view-point for audio signal processing. On the other side, basic psychoacoustic concepts are presented to stimulate mathematicians to apply their knowledge in this field.

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

Many audio applications rely on filter banks (FBs) to analyze, process, and re-synthesize sounds. To approximate the auditory frequency resolution in the signal chain, some applications rely on perceptually motivated FBs, the gammatone FB being a popular example. However, most perceptually motivated FBs only allow partial signal reconstruction at high redundancies and/or do not have good resistance to sub-channel processing. This paper introduces an oversampled perceptually motivated FB enabling perfect reconstruction, efficient FB design, and adaptable redundancy. The filters are directly constructed in the frequency domain and linearly distributed on a perceptual frequency scale (e.g. ERB, Bark, or Mel scale). The proposed design allows for various filter shapes, uniform or non-uniform FB setting, and large down-sampling factors. For redundancies $\geq$ 3 perfect reconstruction is achieved by computing the canonical dual FB analytically. For lower redundancies perfect reconstruction is achieved using an iterative method. Experiments show performance improvements of the proposed approach when compared to the gammatone FB in terms of reconstruction error and resistance to sub-channel processing, especially at low redundancies.

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Representing and counting the subgroups of the group Z_m x Z_n

We deduce a simple representation and the invariant factor decompositions of the subgroups of the group $\Bbb{Z}_m \times \Bbb{Z}_n$, where $m$ and $n$ are arbitrary positive integers. We obtain formulas for the total number of subgroups and the number of subgroups of a given order.

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Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

We consider the problem of designing spectral graph filters for the construction of dictionaries of atoms that can be used to efficiently represent signals residing on weighted graphs. While the filters used in previous spectral graph wavelet constructions are only adapted to the length of the spectrum, the filters proposed in this paper are adapted to the distribution of graph Laplacian eigenvalues, and therefore lead to atoms with better discriminatory power. Our approach is to first characterize a family of systems of uniformly translated kernels in the graph spectral domain that give rise to tight frames of atoms generated via generalized translation on the graph. We then warp the uniform translates with a function that approximates the cumulative spectral density function of the graph Laplacian eigenvalues. We use this approach to construct computationally efficient, spectrum-adapted, tight vertex-frequency and graph wavelet frames. We give numerous examples of the resulting spectrum-adapted graph filters, and also present an illustrative example of vertex-frequency analysis using the proposed construction.

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Efficient algorithms for discrete Gabor transforms on a nonseparable lattice

The Discrete Gabor Transform (DGT) is the most commonly used transform for signal analysis and synthesis using a linear frequency scale. It turns out that the involved operators are rich in structure if one samples the discrete phase space on a subgroup. Most of the literature focuses on separable subgroups, in this paper we will survey existing methods for a generalization to arbitrary groups, as well as present an improvement on existing methods. Comparisons are made with respect to the computational complexity, and the running time of optimized implementations in the C programming language. The new algorithms have the lowest known computational complexity for nonseparable lattices and the implementations are freely available for download. By summarizing general background information on the state of the art, this article can also be seen as a research survey, sharing with the readers experience in the numerical work in Gabor analysis.

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Structure of nonstationary Gabor frames and their dual systems

We investigate the structural properties of dual systems for nonstationary Gabor frames. In particular, we prove that some inverse nonstationary Gabor frame operators admit a Walnut-like representation, i.e. the operator acting on a function can be described by weighted translates of that function, even when the original frame operator is not diagonal. In this case, which only occurs when compactly supported window functions are used, the canonical dual frame partially inherits the structure of the original frame, with differences that we describe in detail. Moreover, we determine a sufficient condition for a pair of nonstationary Gabor frames to form dual frames. The equivalence of this condition to the duality of the involved systems is shown under some weak restrictions. It is then applied in a simple setup, to prove the existence of dual pairs of nonstationary Gabor systems with non-diagonal frame operator. A discussion of the results, restricted to the classical case of regular Gabor systems, precedes the statement of the general results. Here, we also explore a connection to recent work of Christensen, Kim and Kim on Gabor frames with compactly supported window function.

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A framework for invertible, real-time constant-Q transforms

Audio signal processing frequently requires time-frequency representations and in many applications, a non-linear spacing of frequency-bands is preferable. This paper introduces a framework for efficient implementation of invertible signal transforms allowing for non-uniform and in particular non-linear frequency resolution. Non-uniformity in frequency is realized by applying nonstationary Gabor frames with adaptivity in the frequency domain. The realization of a perfectly invertible constant-Q transform is described in detail. To achieve real-time processing, independent of signal length, slice-wise processing of the full input signal is proposed and referred to as sliCQ transform. By applying frame theory and FFT-based processing, the presented approach overcomes computational inefficiency and lack of invertibility of classical constant-Q transform implementations. Numerical simulations evaluate the efficiency of the proposed algorithm and the method's applicability is illustrated by experiments on real-life audio signals.

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