Search Results for author: Peter Gerstoft

Found 18 papers, 2 papers with code

Source localization in an ocean waveguide using supervised machine learning

no code implementations29 Jan 2017 Haiqiang Niu, Emma Reeves, Peter Gerstoft

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data.

BIG-bench Machine Learning General Classification

Travel time tomography with adaptive dictionaries

no code implementations16 Dec 2017 Michael Bianco, Peter Gerstoft

The local model considers small-scale variations using a sparsity constraint and the global model considers larger-scale features constrained using $\ell_2$ regularization.

Dictionary Learning

Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors

no code implementations17 Mar 2018 Jiang Zhu, Qi Zhang, Peter Gerstoft, Mihai-Alin Badiu, Zhiwei Xu

In this paper, the line spectral estimation (LSE) problem with multiple measurement vectors (MMVs) is studied utilizing the Bayesian methods.

Information Theory Information Theory

Sound source ranging using a feed-forward neural network with fitting-based early stopping

no code implementations1 Apr 2019 Jing Chi, Xiaolei Li, Haozhong Wang, Dazhi Gao, Peter Gerstoft

Based on FEAST, when the evaluated range error of the FNN reaches the minimum on test data, stopping training, which will help to improve the ranging accuracy of the FNN on the test data.

Machine learning in acoustics: theory and applications

no code implementations11 May 2019 Michael J. Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A. Roch, Sharon Gannot, Charles-Alban Deledalle

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science.

BIG-bench Machine Learning

Semi-supervised source localization with deep generative modeling

no code implementations27 May 2020 Michael J. Bianco, Sharon Gannot, Peter Gerstoft

We propose a semi-supervised localization approach based on deep generative modeling with variational autoencoders (VAEs).

SSLIDE: Sound Source Localization for Indoors based on Deep Learning

no code implementations27 Oct 2020 Yifan Wu, Roshan Ayyalasomayajula, Michael J. Bianco, Dinesh Bharadia, Peter Gerstoft

This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space.

Deep embedded clustering of coral reef bioacoustics

no code implementations17 Dec 2020 Emma Ozanich, Aaron Thode, Peter Gerstoft, Lauren A. Freeman, Simon Freeman

DEC, GMM, and conventional clustering were tested on simulated datasets of fish pulse calls (fish) and whale song units (whale) with randomized bandwidth, duration, and SNR.

Clustering Deep Clustering

Semi-supervised source localization in reverberant environments with deep generative modeling

no code implementations26 Jan 2021 Michael J. Bianco, Sharon Gannot, Efren Fernandez-Grande, Peter Gerstoft

As far as we are aware, our paper presents the first approach to modeling the physics of acoustic propagation using deep generative modeling.

Alternating projections gridless covariance-based estimation for DOA

no code implementations12 Feb 2021 Yongsung Park, Peter Gerstoft

We present a gridless sparse iterative covariance-based estimation method based on alternating projections for direction-of-arrival (DOA) estimation.

Audio scene monitoring using redundant ad-hoc microphone array networks

no code implementations2 Mar 2021 Peter Gerstoft, Yihan Hu, Michael J. Bianco, Chaitanya Patil, Ardel Alegre, Yoav Freund, Francois Grondin

The DOAs are fed to a fusion center, concatenated, and used to perform the localization based on two proposed methods, which require only few labeled source locations (anchor points) for training.

Gridless DOA Estimation with Multiple Frequencies

no code implementations13 Jul 2022 Yifan Wu, Michael B. Wakin, Peter Gerstoft

Direction-of-arrival (DOA) estimation is widely applied in acoustic source localization.

Graph-based sequential beamforming

1 code implementation26 Aug 2022 Yongsung Park, Florian Meyer, Peter Gerstoft

At each time step, belief propagation predicts the number of DOAs and their DOAs using posterior probability density functions (pdfs) from the previous time and a different Bernoulli-von Mises state transition model.

Bayesian Inference

Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks

1 code implementation8 Nov 2022 Daniel Romero, Peter Gerstoft, Hadi Givehchian, Dinesh Bharadia

In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user.

Spoofing Attack Detection in the Physical Layer with Robustness to User Movement

no code implementations17 Oct 2023 Daniel Romero, Tien Ngoc Ha, Peter Gerstoft

In a spoofing attack, an attacker impersonates a legitimate user to access or modify data belonging to the latter.

Community Detection

Deep Learning based Spatially Dependent Acoustical Properties Recovery

no code implementations17 Oct 2023 Ruixian Liu, Peter Gerstoft

The physics-informed neural network (PINN) is capable of recovering partial differential equation (PDE) coefficients that remain constant throughout the spatial domain directly from physical measurements.

Non-uniform Array and Frequency Spacing for Regularization-free Gridless DOA

no code implementations12 Jan 2024 Yifan Wu, Michael B. Wakin, Peter Gerstoft

The DOA is retrieved using a Vandermonde decomposition on the Toeplitz matrix obtained from the solution of the SDP.

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