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Ivan Dokmanic

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Related publications (40)

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We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the source ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

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Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize point ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Shapes From Echoes: Uniqueness From Point-to-Plane Distance Matrices

Martin Vetterli, Ivan Dokmanic, Miranda Krekovic

We study the problem of localizing a configuration of points and planes from the collection of point-to-plane distances. This problem models simultaneous localization and mapping from acoustic echoes as well as the & x201C;structure from sound & x201D; app ...

2020

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In this paper, we propose kinetic Euclidean distance matrices (KEDMs) a new algebraic tool for localization of moving points from spatio temporal distance measurements. KEDMs are inspired by the well-known Euclidean distance matrices (EDM) which model stat ...

IEEE2019

Conventional approaches to sound source localization require at least two microphones. It is known, however, that people with unilateral hearing loss can also localize sounds. Monaural localization is possible thanks to the scattering by the head, though i ...

2018

Pyroomacoustics: A Python Package For Audio Room Simulation And Array Processing Algorithms

Eric Bezzam, Ivan Dokmanic, Robin Scheibler

We present pyroomacoustics, a software package aimed at the rapid development and testing of audio array processing algorithms. The content of the package can be divided into three main components: an intuitive Python object-oriented interface to quickly c ...

IEEE2018

Structure from sound with incomplete data

Martin Vetterli, Ivan Dokmanic, Gilles Baechler, Miranda Krekovic

In this paper, we consider the problem of jointly localizing a microphone array and identifying the direction of arrival of acoustic events. Under the assumption that the sources are in the far field, this problem can be formulated as a constrained low-ran ...

2018

Omnidirectional bats, point-to-plane distances, and the price of uniqueness

Martin Vetterli, Ivan Dokmanic, Miranda Krekovic

We study simultaneous localization and mapping with a device that uses reflections to measure its distance from walls. Such a device can be realized acoustically with a synchronized collocated source and receiver; it behaves like a bat with no capacity for ...

Ieee2017

FRIDA: FRI-Based DOA Estimation For Arbitrary Array Layouts

Martin Vetterli, Eric Bezzam, Ivan Dokmanic, Robin Scheibler, Hanjie Pan

In this paper we present FRIDA—an algorithm for estimating directions of arrival of multiple wideband sound sources. FRIDA combines multi-band information coherently and achieves state- of-the-art resolution at extremely low signal-to-noise ratios. It work ...

IEEE2017

Acoustic DoA Estimation by One Unsophisticated Sensor

Martin Vetterli, Ivan Dokmanic, Dalia Salem Hassan Fahmy El Badawy

We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend t ...

Springer Verlag2017