LCAV/FukushimaInverse-ICASSP2013

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Created over 11 years ago
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LCAV/FukushimaInverse-ICASSP2013

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The Fukushima inverse problem. Code and data to compute emissions of radioactive material at the Fukushima Dai-Ichi power plant during the Great Tohoku Earthquake of 3/11 2011. This code allows to reproduce results published at ICASSP 2013.

pyroomacoustics

Pyroomacoustics is a package for audio signal processing for indoor applications. It was developed as a fast prototyping platform for beamforming algorithms in indoor scenarios.

FRIDA

A high-resolution direction-of-arrival finding algorithm relying on finite rate of innovation sampling with a robust reconstruction algorithm.

DSPNumex

Numerical examples examples for the DSP class by Martin Vetterli and Paolo Prandoni on coursera.

SignalsOfTheDay

This is a collection of signals interesting for historical or cultural reasons. Each signal is described and analyzed in an [ipython notebook](http://ipython.org/notebook.html).

AcousticRakeReceiver

The acoustic rake receiver, a microphone beamformer that uses echoes to improve the noise and interference suppression. Python code to reproduce all the results from Raking the Cocktail Party by Ivan Dokmanic, Robin Scheibler, and Martin Vetterli.

LenslessPiCam

Lensless imaging toolkit. Complete tutorial: https://go.epfl.ch/lenslesspicam

dsp-labs

GitBook for COM-303 Practical Sessions at EPFL:

edmbox

Matlab routines for Euclidean distance matrices

audioROS

ROS2 stack for audio processing

Pyramic

A compact array with a large number of microphones and a flexible geometry.

linvpy

CompactSix

A compact array with 6 MEMS microphones relying on the BeagleBoneBlack and the STA321MP audio processor.

pylocus

Localization package using distance and/or angle measurements

SparseFHT

A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain -- Implementation

TimeDomainAcousticRakeReceiver

Software design and analysis tools for the acoustic rake receiver, a microphone beamformer that uses echoes to improve the noise and interference suppression. Python code to reproduce all the results from Raking Echoes in the Time Domain by Robin Scheibler, Ivan Dokmanic, and Martin Vetterli.

localization-icassp2018

Code of paper "Combining range and direction for improved localization" presented at ICASSP2018

easy-dsp

A browser based interface for real-time DSP on popular embedded platforms.

continuous-localization

Code of paper "Relax and Recover: Guaranteed Range-Only Continuous Localization"

MP3Lab

This iPython Notebook is created as a part of the Digital Signal Processing (DSP) class offered at EPFL to explain the process of MP3 encoding, with a focus on the DSP parts of the encoder.

sketchrls

Sketch RLS is an adaptive filtering algorithm that brings sketching ideas into the classical recursive least squares algorithm. This is the python implementation of the algorithm.

lippmann-photography

Copy of the Historical Lippmann repository that was lost in the depths of C4science

Jupyter Notebook

audio-localization-dataset

Datasets for acoustic echolocation and sound source localization on Crazyflie drone and e-puck robot.

MFSP_notebooks

Notebooks used to teach mathematical foundations of signal processing

Jupyter Notebook

crazyflie-audio

Code for recording audio on Crazyflie

PyTurntableBK9640

Python driver for the Bruel & Kjaer turntable system type 9640.

AcousticRobot

Routines to control a humanoid echolocator robot.

RegularizedTauEstimator

Reproducible Research material for : A new robust and ifficient estimator for ill-conditioned linear inverse problems with outliers.

fingerings

Jupyter Notebook

COM202

Signal Processing Python labs for COM 202

Jupyter Notebook

surface-reconstruction

Code for papers 'Sampling at unknown locations: Uniqueness and reconstruction under constraints' and 'Sampling at unknown locations, with an application in surface retrieval'

Jupyter Notebook

mooc-hwlab

Gitbook for the hardware lab in the DSP4 MOOC

mkl_fft

Python wrappers for Intel MKL FFT routines

KrylovFRITools

We present proof of concept code - MATLAB and Python - for identification of the union of subspaces in FRI estimation problems when the number of measurements greatly exceeds the dimension of the model.