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Found 10 papers, 2 papers with code
A Diffusion-Based Generative Equalizer for Music Restoration
This paper presents a novel approach to audio restoration, focusing on the enhancement of low-quality music recordings, and in particular historical ones.
Multi-Source Localization and Data Association for Time-Difference of Arrival Measurements
That is, it is not known which of the TDOA measurements correspond to the same source.
Room Impulse Response Estimation using Optimal Transport: Simulation-Informed Inference
The ability to accurately estimate room impulse responses (RIRs) is integral to many applications of spatial audio processing.
Blind Audio Bandwidth Extension: A Diffusion-Based Zero-Shot Approach
In cases where the lowpass degradation is unknown, such as in restoring historical audio recordings, this becomes a blind problem.
Distributed Adaptive Norm Estimation for Blind System Identification in Wireless Sensor Networks
Distributed signal-processing algorithms in (wireless) sensor networks often aim to decentralize processing tasks to reduce communication cost and computational complexity or avoid reliance on a single device (i. e., fusion center) for processing.
An efficient solver for designing optimal sampling schemes
In this short paper, we describe an efficient numerical solver for the optimal sampling problem considered in "Designing Sampling Schemes for Multi-Dimensional Data".
Quantifying and Computing Covariance Uncertainty
In this work, we consider the problem of bounding the values of a covariance function corresponding to a continuous-time stationary stochastic process or signal.
Determining Joint Periodicities in Multi-time Data With Sampling Uncertainties
In this work, we introduce a novel approach for determining a joint sparse spectrum from several non-uniformly sampled data sets, where each data set is assumed to have its own, possibly disjoint, and only partially known, sampling times.
Mixed-Spectrum Signals -- Discrete Approximations and Variance Expressions for Covariance Estimates
Furthermore, we consider approximating signals with arbitrary spectral densities by sequences of singular spectrum, i. e., sinusoidal, processes, and derive the limiting behavior of covariance estimates as both the sample size and the number of sinusoidal components tend to infinity.
Defining Fundamental Frequency for Almost Harmonic Signals
Herein, we provide three different definitions of a concept of fundamental frequency for such inharmonic signals and study the implications of the different choices for modeling and estimation.
