Search Results for author: Peter Gerstoft

Found 11 papers, 0 papers with code

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.

Affine Transformation

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.

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.

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.

Deep Clustering

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.

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).

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.

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.

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

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

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.

General Classification

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