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

Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment (ICCV2021)

Code and data of the Fine-Grained R2R Dataset proposed in paper Sub-Instruction Aware Vision-and-Language Navigation

Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

This repository contains demos I made with the Transformers library by HuggingFace.

Official Implementation of "Transformers Can Do Bayesian Inference"

Sleep staging from ECG, assisted with EEG

Notepy is a full-featured Notepad Python app

(IEEE TIP 2021) Regularized Densely-connected Pyramid Network for Salient Instance Segmentation

Simple node deletion tool for onnx.

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch

Hand Gesture Volume Control is AIML based project which uses image processing to control the volume of your Computer.

Segmentation models with pretrained backbones. PyTorch.

PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"

Dahua Camera and Doorbell Home Assistant Integration

Implementation of OpenAI paper with Simple Noise Scale on Fastai V2

Simple data balancing baselines for worst-group-accuracy benchmarks.

DetCo: Unsupervised Contrastive Learning for Object Detection

Boosted neural network for tabular data

A sketch extractor for anime/illustration.

The `calc` function

The `plot` function