Adaptive time segmentation for improved signal model parameter estimation for a single-source scenario

Estimating the parameters that describe the acoustic scene is very important for many microphone array applications. For example, consider the power spectral densities (PSDs) or relative acoustic transfer functions (RTFs) that are required when estimating a particular sound source using multi-microphone noise reduction. State-of-the-art...

Simplicial Trend Filtering (Invited Paper)

Reconstructing simplicial signals, e.g., signals defined on nodes, edges, triangles, etc., of a network, from (partial) noisy observation is of interest in water/traffic flow estimation or currency exchange markets. Typically, this concerns solving a regularised problem w.r.t. the l2 norm of the divergence or the curl of the signal, i.e., the...

A Delayed and Subsampled Wideband Sparse Array for Joint Angle and Frequency Estimation

In this paper we consider the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation, where a number of uncorrelated narrowband sources spread over a wide frequency band impinge on a sparse linear array (SLA). To overcome the sampling rate bottleneck for wideband spectrum sensing, we rely on sub-Nyquist sampling for...

The effectiveness of self-supervised representation learning in zero-resource subword modeling

For a language with no transcribed speech available (the zero-resource scenario), conventional acoustic modeling algorithms are not applicable. Recently, zero-resource acoustic modeling has gained much interest. One research problem is unsupervised subword modeling (USM), i.e., learning a feature representation that can distinguish subword units...

Online Graph Learning From Time-Varying Structural Equation Models

Topology identification is an important problem across many disciplines, since it reveals pairwise interactions among entities and can be used to interpret graph data. In many scenarios, however, this (unknown) topology is time-varying, rendering the problem even harder. In this paper, we focus on a time-varying version of the structural...

Rational Chebyshev Graph Filters

This paper proposes rational Chebyshev graph filters to approximate step graph spectral responses with arbitrary precision, which are of interest in graph filter banks and spectral clustering. The proposed method relies on the well-known Chebyshev filters of the first kind and on a domain transform of the angular frequencies to the graph...

Joint Ranging and Clock Synchronization for Dense Heterogeneous IoT Networks

Synchronization and ranging in internet of things (IoT) networks are challenging due to the narrowband nature of signals used for communication between IoT nodes. Recently, several estimators for range estimation using phase difference of arrival (PDoA) measurements of narrowband signals have been proposed. However, these estimators are based...

On the Limits of Finite-Time Distributed Consensus Through Successive Local Linear Operations

In this work, we explore the limits of finite-time distributed consensus through the intersection of graph filters and matrix function theory. We focus on algorithms capable to compute the consensus exactly through filtering operations over a graph, and that have been proven to converge in finite time. In this context, we show that there...

Observing Bandlimited Graph Processes from Subsampled Measurements

This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process...

Convolutional Graph Neural Networks

Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images. The use of convolutions, however, makes them unsuitable for...

Calibration techniques for single-sensor ultrasound imaging with a coding mask

We consider a model-based ultrasound imaging scenario using a single transducer with a coding mask, and assume that the pulse-echo model is erroneously estimated, resulting in decreased imaging performance. Although the pulse-echo Green's function to each pixel has to be measured to obtain a good model, typically only forward-field...

Spatial compression in ultrasound imaging

High quality three dimensional ultrasound imaging is typically attained by increasing the amount of sensors, resulting in complex hardware. Compressing measurements before sensing addresses this problem, and could enable new clinical applications. We have developed an analogue compression technique, by positioning a plastic coding mask in...

Antenna and pulse selection for colocated MIMO radar

Multiple input multiple output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. However, the increased hardware cost (due to multiple transmitters and receivers), power consumption (due to multiple transmitters and pulses), and computational complexity (due to numerous pulses) form...

An Ideal-Theoretic Criterion for Localization of an Unknown Number of Sources

Source localization is among the most fundamental problems in statistical signal processing. Methods which rely on the orthogonality of the signal and noise subspaces, such as Pisarenko’s method, MUSIC, and root-MUSIC are some of the most widely used algorithms to solve this problem. As a common feature, these methods require both a-priori...