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

Overview

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

DOI

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.

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.
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

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

This repository contains a pytorch implementation of
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

This repo contains the code and data used in the paper
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

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
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

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

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

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

Supplementary code for the paper
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

Releases(second_release)
Owner
Jörg Encke
Jörg Encke
arxiv-sanity, but very lite, simply providing the core value proposition of the ability to tag arxiv papers of interest and have the program recommend similar papers.

arxiv-sanity, but very lite, simply providing the core value proposition of the ability to tag arxiv papers of interest and have the program recommend similar papers.

Andrej 671 Dec 31, 2022
Listing arxiv - Personalized list of today's articles from ArXiv

Personalized list of today's articles from ArXiv Print and/or send to your gmail

Lilianne Nakazono 5 Jun 17, 2022
Arxiv harvester - Poor man's simple harvester for arXiv resources

Poor man's simple harvester for arXiv resources This modest Python script takes

Patrice Lopez 5 Oct 18, 2022
Code in conjunction with the publication 'Contrastive Representation Learning for Hand Shape Estimation'

HanCo Dataset & Contrastive Representation Learning for Hand Shape Estimation Code in conjunction with the publication: Contrastive Representation Lea

Computer Vision Group, Albert-Ludwigs-Universität Freiburg 38 Dec 13, 2022
This GitHub repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.'

About Repository This repository contains code used for plots in NeurIPS 2021 paper 'Stochastic Multi-Armed Bandits with Control Variates.' About Code

Arun Verma 1 Nov 9, 2021
This project contains an implemented version of Face Detection using OpenCV and Mediapipe. This is a code snippet and can be used in projects.

Live-Face-Detection Project Description: In this project, we will be using the live video feed from the camera to detect Faces. It will also detect so

Hassan Shahzad 3 Oct 2, 2021
constructing maps of intellectual influence from publication data

Influencemap Project @ ANU Influence in the academic communities has been an area of interest for researchers. This can be seen in the popularity of a

CS Metrics 13 Jun 18, 2022
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 8, 2022
This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

TSForecasting This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the tim

Rakshitha Godahewa 80 Dec 30, 2022
An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow implementation of SERank model. The code is developed based on TF-Ranking.

SERank An efficient and effective learning to rank algorithm by mining information across ranking candidates. This repository contains the tensorflow

Zhihu 44 Oct 20, 2022