This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637

Last update: Oct 14, 2022

Related tags

Deep Learning binaural-detection-model

Overview

This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637

Dependencies

The model depends on the following python packages:

numpy
scipy
pandas
tables
matplotlib

All of witch should be included in popular python distributions such as Anaconda. If you are using virtual environments with pip, you can install all requirements by running:

pip install -r requirements

Repository Structure

The repository contains all scripts to run and plot the experiments discussed in the manuscript. It also contains a data.h5 file which contains pre-calculated results in HDF5 format. There are also two script calc_all.py and plot_all.py which will run an plot all experiments respectively.

How to run individual experiments.

The experiment subfolder acts as a python package. Experiments are best loaded individually To calculate and plot the results for the experiment of Lanford & Jeffress 1964 one would thus run:

from experiments import langford1964

langford1964.calc() # run the experiment store results in data.h5
langford1964.plot() # plot the results which are loaded from the data.h5 file

The `calc` function

Calling the calc function without parameters runs the model with the parameters as stated in the manuscript. Model parameters can, however, be easily changed by setting the parameters

rho_hat
bin_noise
mon_noise

for example:

langford1964.calc(rho_hat=0.95, bin_noise=0.33, mon_noise=0.70)

Be aware that the calc function overwrites previous results that might be stored in data.h5 to prevent this, set the save parameter to False:

langford1964.calc(rho_hat=0.95, bin_noise=0.33, mon_noise=0.70, save=False)

Alternatively, one can also provide the filename for a new buffer file:

langford1964.calc(rho_hat=0.95, bin_noise=0.33, mon_noise=0.70, save='newdata.h5')

The `plot` function

As the name suggests, the plot function plots the model results. By default, the function plots pre-calculated values as stored in the data.h5 file. One can provide the file paramter to load data from another file:

langford1964.plot(file='newdata.h5')

Model Structure

All model code is contained within the experiments folder. The actual model is implemented in model.py.

Individual experiments are split into subfolders named following the structure authorYEAR. The folder langford1964 thus contains scripts for the experiment of Langford & Jeffress 1964. Each of these folders contains a calc.py file which includes the code for running the calculations and saving the results in a buffer file called data.h5. The plot.py file in the subfolder then contains the code for plotting the results from the buffer file as well as the experimental results.

Please be aware that these files only provide the functions for calculating and plotting the results and can not be called directly.

Releases(second_release)

second_release(Nov 9, 2021)

Source code(tar.gz)
Source code(zip)
first_release(Nov 3, 2021)

A first release of the code
Source code(tar.gz)
Source code(zip)
final_release(Sep 16, 2022)

Final release of the model code used in the publication
Source code(tar.gz)
Source code(zip)

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

1 Jan 1, 2022

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021) This repository is for the following paper: "Investigating Attention

52 Nov 19, 2022

This repository contains a pytorch implementation of "HeadNeRF: A Real-time NeRF-based Parametric Head Model (CVPR 2022)".

HeadNeRF: A Real-time NeRF-based Parametric Head Model This repository contains a pytorch implementation of "HeadNeRF: A Real-time NeRF-based Parametr

294 Jan 1, 2023

This repo contains the code and data used in the paper "Wizard of Search Engine: Access to Information Through Conversations with Search Engines"

Wizard of Search Engine: Access to Information Through Conversations with Search Engines by Pengjie Ren, Zhongkun Liu, Xiaomeng Song, Hongtao Tian, Zh

19 Oct 27, 2022

An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

SVM Données Une base d’images contient 490 images pour l’apprentissage (400 voitures et 90 bateaux), et encore 21 images pour fait des tests. Prétrait

3 Nov 30, 2021

Object detection using yolo-tiny model and opencv used as backend

Object detection Algorithm used : Yolo algorithm Backend : opencv Library required: opencv = 4.5.4-dev' Quick Overview about structure 1) main.py Load

2 Jul 6, 2022

Code for our method RePRI for Few-Shot Segmentation. Paper at http://arxiv.org/abs/2012.06166

Region Proportion Regularized Inference (RePRI) for Few-Shot Segmentation In this repo, we provide the code for our paper : "Few-Shot Segmentation Wit

138 Dec 12, 2022

This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf).

TransBTS: Multimodal Brain Tumor Segmentation Using Transformer This repo is the official implementation for TransBTS: Multimodal Brain Tumor Segmenta

247 Dec 28, 2022

Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

25 Aug 12, 2021

This repository contains the code for the binaural-detection model used in the publication arXiv:2111.04637

Related tags

Overview

Dependencies

Repository Structure

How to run individual experiments.

The calc function

The plot function

Model Structure

You might also like...

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

This repository contains a pytorch implementation of "HeadNeRF: A Real-time NeRF-based Parametric Head Model (CVPR 2022)".

This repo contains the code and data used in the paper "Wizard of Search Engine: Access to Information Through Conversations with Search Engines"

An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

Object detection using yolo-tiny model and opencv used as backend

Code for our method RePRI for Few-Shot Segmentation. Paper at http://arxiv.org/abs/2012.06166

This repo provides the official code for TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/pdf/2103.04430.pdf).

Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Releases(second_release)

second_release(Nov 9, 2021)

first_release(Nov 3, 2021)

final_release(Sep 16, 2022)

Owner

Jörg Encke

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation

Bayesian algorithm execution (BAX)

GANsformer: Generative Adversarial Transformers Drew A

A PyTorch implementation of the continual learning experiments with deep neural networks

Stratified Transformer for 3D Point Cloud Segmentation (CVPR 2022)

Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

This repo contains the code and data used in the paper "Wizard of Search Engine: Access to Information Through Conversations with Search Engines"

A PyTorch Implementation of Single Shot Scale-invariant Face Detector.

Picasso: a methods for embedding points in 2D in a way that respects distances while fitting a user-specified shape.

Pytorch implementation of DeepMind's differentiable neural computer paper.

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

Affine / perspective transformation in Pose Estimation with Tensorflow 2

Self-describing JSON-RPC services made easy

tinykernel - A minimal Python kernel so you can run Python in your Python

Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

Convolutional Neural Network for Text Classification in Tensorflow

🔥 TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement"

Training DiffWave using variational method from Variational Diffusion Models.

ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

The code of paper 'Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection'

The `calc` function

The `plot` function